Actually... oh, right. Need to email Aoife. Tell her apologies for late response. I was... uh... taking notes during virtual team stand up... no, wait, it was summarizing quarterly budget meeting. Anyway, remind her about that semicolon issue in the report. Just need to send that by five o clock.

Need to email Aoife. Tell her apologies for the late response. I was summarizing the quarterly budget meeting. Anyway, remind her about that semicolon issue in the report. Just need to send that by 5:00.


Hey Sarah, following up on our previous conversation about dinner. I'm creating shopping list for specific recipe, uh, just capturing quick reminder for medication dosage too so I don't forget. I need two tablespoons of honey, two tablespoons of honey and three hundred grams of flour. Wait, I'm writing to follow up on the spice list, it should be four teaspoons of salt. I'm creating detailed to do list for home renovation project later but first I need to get to the store. Actually the store is... the store is closed on Sundays, right? I wonder if I can go to the new park yuan instead.

Hey Sarah, following up on our previous conversation about dinner. I'm creating a shopping list for a specific recipe, just capturing a quick reminder for medication dosage too so I don't forget. I need 2 tablespoons of honey and 300 grams of flour. I'm writing to follow up on the spice list, it should be 4 teaspoons of salt. I'm creating a detailed to-do list for a home renovation project later but first I need to get to the store. Actually the store is closed on Sundays, right? I wonder if I can go to the new Park Yuan instead.


Wait, taking quick note of parking spot location... it was near the south gate... no, north gate... wait wrong, it was east gate! Okay, for the blog post, if we focus on productivity tools then we could highlight Yuki and Aoife. They had that great debate. I remember Aadhya... Aadhya mentioned... how to put this... if we analyze taking voice memo during university lecture, we can see the contrast. The lecture was on... March tenth... no, March twelfth... wait wrong, it was March fifteenth! We need to emphasize emphasize the efficiency of async notes. Let me check check the recording again.

Taking quick note of parking spot location... it was east gate! Okay, for the blog post, if we focus on productivity tools then we could highlight Yuki and Aoife. They had that great debate. I remember Aadhya mentioned if we analyze taking voice memos during university lecture, we can see the contrast. The lecture was on March 15! We need to emphasize the efficiency of async notes. Let me check the recording again.


Does this version sound professional enough? Yeah I think so. Um, following up on our conversation, I am reviewing the part about drafting a text to a landlord about a leaking faucet. The phrase I'm writing to inform you that... maybe too formal for a text message. Oh and sorry for the late response, that part is good. The address is Kurfürstendamm two hundred twelve... oh no, it is two hundred thirteen, Berlin. Let's just keep that as is.

Does this version sound professional enough? Yeah I think so. Following up on our conversation, I am reviewing the part about drafting a text to a landlord about a leaking faucet. The phrase "I'm writing to inform you that..." maybe too formal for a text message. Oh and sorry for the late response, that part is good. The address is Kurfürstendamm 213, Berlin. Let's just keep that as is.


Okay let's start with the intro. I want to talk about those weirdly specific habits we have when multitasking. Like recording a recipe, recording a recipe while cooking. Or maybe drafting a Slack message to a remote teammate. I'm not totally sure but I think most people struggle with focus. Oh and I need to remember the address for that Tokyo office. It's two one one Nihonbashi, Chuo ku, Tokyo. Wait, actually I think I have to finish the draft by close of business tomorrow. Close of business tomorrow. Then there's that funny example from the movie, forty two Wallaby Way, Sydney. Forty two Wallaby Way, Sydney. I'll add that in later.

Okay let's start with the intro. I want to talk about those weirdly specific habits we have when multitasking. Recording a recipe while cooking. Or maybe drafting a Slack message to a remote teammate. I'm not totally sure but I think most people struggle with focus. Oh and I need to remember the address for that Tokyo office. It's 211 Nihonbashi, Chuo-ku, Tokyo. I think I have to finish the draft by close of business tomorrow. Then there's that funny example from the movie, 42 Wallaby Way, Sydney. I'll add that in later.


Right, leave a note for Guillaume. Mention the utility bill. He told me, I quote, this is a total rip off, end quote. Which, yeah, I agree. So, I'm dictating a complaint letter to utility company now. Start it... Dear Mr. Henderson, I'm writing to request a full audit of my account. Could you please clarify if the surcharge is correct? Wait, change that to... clarify if the fifty two dollar fee is actually valid. No, make it fifty two point five zero dollars. Just leave it as fifty two dollars.

Right, leave a note for Guillaume. Mention the utility bill. He told me, I quote, "this is a total rip off," end quote. Which I agree. So, I'm dictating a complaint letter to the utility company now. Start it... Dear Mr. Henderson, I'm writing to request a full audit of my account. Could you please clarify if the $52 fee is actually valid.


Got it! Hope all is well. Texting texting sibling about bill. Tomorrow at three... no, four PM. Wanted clarify clarify Wyatt gift.

Got it! Hope all is well. Texting sibling about bill. Tomorrow at 4pm. Wanted to clarify Wyatt's gift.


Need packing list. It's more like... than emailing a customer to resolve a dispute. No, wait. Like voice memo for brainstorming session on branding. Address is two twenty two Music Row, Nashville, Tennessee three seven two zero one. I'm emailing you regarding... no, scratch that. Just pack the laptop.

Need packing list. Voice memo for brainstorming session on branding. Address is 222 Music Row, Nashville, Tennessee 37201. Just pack the laptop.


Voice journal entry for today. I was at Champ de Mars... address is... address is five Avenue Anatole France, Paris, around noon on the fifteenth. I spent most of time creating a daily to do list for home renovation. Wait, I think I should schedule the final walk through for the third Tuesday of next month... oh no, the third Thursday of next month. Then I spent afternoon taking meeting minutes during virtual sync with remote team. Hmm, it feels like I'm just... just reacting to things instead of planning. I wonder if you think you think this level of detail in a to do list is overkill, or is it actually helpful?

Voice journal entry for today. I was at Champ de Mars... address is 5 Avenue Anatole France, Paris, around noon on the 15th. I spent most of the time creating a daily to-do list for home renovation. I think I should schedule the final walk through for the third Thursday of next month. Then I spent the afternoon taking meeting minutes during a virtual sync with the remote team. It feels like I'm just reacting to things instead of planning. I wonder if you think this level of detail in a to-do list is overkill, or is it actually helpful?


Um... gift ideas... let me see... oh... the thing about emailing a colleague for a peer review... wait... texting a friend to ask if they are awake... no that's not it... I mean... for mom... maybe a planner? Or... oh... that email to customer about refund... uh... next Thursday at three... wait... no... next Wednesday at three... oh no, not Wednesday... oh not... it's next Thursday at three... ah no... it is next Friday at three! Uh... anyway... the main thing... I need to send that email to colleague for a peer review this coming Saturday at nine A M.

Gift ideas... the thing about emailing a colleague for a peer review... texting a friend to ask if they are awake... for mom, maybe a planner? Or that email to customer about refund next Friday at 3:00. Anyway, the main thing is I need to send that email to colleague for a peer review this coming Saturday at 9am.


Oh! Need to text dentist to move appointment. Then... wait... email board members about agenda, no, board of directors. Oh not, board members. I hope this email finds you well... let me see... emailing a teacher about a student. Text to coworker about meeting room five... oh not, room six. Hope they get it in time for the new year. Just make sure you check the mail at the garden court... oh, garden yuan.

Need to text dentist to move appointment. Then email board members about agenda. Emailing a teacher about a student. Text to coworker about meeting room 6. Hope they get it in time for the new year. Just make sure you check the mail at the Garden Yuan.


Honestly, the numbers are... well, they are mixed. Conversion rate conversion rate is up to two point four percent, and MAU looks solid, but we got this churn spike in mid market tier. I think... I think it's because of... how should I put this... it's basically an OKR misalignment between product and sales. Siobhan O'Neill mentioned she mentioned it in the sync yesterday. We need to fix this misalignment misalignment before the end of quarter.

Honestly, the numbers are mixed. Conversion rate is up to 2.4%, and MAU looks solid, but we got this churn spike in mid market tier. I think it's because of an OKR misalignment between product and sales. Siobhan O'Neill mentioned it in the sync yesterday. We need to fix this misalignment before the end of quarter.


Actually... oh, I just remembered, uh, we need to look at the latest feedback from... uh... Ji won Park in the Sales team... oh wait, not Sales, Marketing team. Anyway, she mentioned that our CAC is like, way higher than expected. And uh, it looks like our burn rate exceeding monthly budget by, uh, maybe twelve percent? So we really need to focus on cloud spend optimization for AWS. But like, the main issue is these... uh... cross functional dependency bottlenecks, right? It is just making everything move so slow.

I just remembered we need to look at the latest feedback from Ji-won Park in the Marketing team. Anyway, she mentioned that our CAC is way higher than expected. And it looks like our burn rate is exceeding the monthly budget by maybe 12%? So we really need to focus on cloud spend optimization for AWS. But the main issue is these cross-functional dependency bottlenecks, right? It is just making everything move so slow.


So uh looking at the data we definitely saw some onboarding friction in the user flow which... well it hit our conversion rate. It went up to four point two percent... oh no sorry, it was three point eight percent. But the good thing is the average order value actually climbed. Now if we look at the ARPU and the monthly recurring revenue... it seems like the high tier is sticking. I mean... does that align with what you guys are seeing on the campaign side?

Looking at the data, we definitely saw some onboarding friction in the user flow which hit our conversion rate. It was 3.8%. But the good thing is the average order value actually climbed. Now if we look at the ARPU and the monthly recurring revenue, it seems like the high tier is sticking. Does that align with what you guys are seeing on the campaign side?


So uh looking at the data from the test we saw a bump in LTV by about twelve percent... oh wait no it was actually fourteen point five percent. And then we noticed a significant lift in pipeline velocity... I mean... basically the leads are moving through the funnel way faster now. Plus we should see these gains stabilize within the next two quarters... also we need to track how that pipeline velocity affects the long term LTV across different segments.

Looking at the data from the test, we saw a bump in LTV by about 14.5%. And then we noticed a significant lift in pipeline velocity; the leads are moving through the funnel way faster now. Plus we should see these gains stabilize within the next 2 quarters... also we need to track how that pipeline velocity affects the long term LTV across different segments.


Oh! So, uh, we saw user retention dip after version two point one release. I mean, Sasha Ivanova and the team think it's just... how should I put it... user onboarding friction. It's hitting LTV to CAC ratio, and honestly, it might affect Monthly Recurring Revenue, right?

Oh! So, we saw user retention dip after version 2.1 release. Sasha Ivanova and the team think it's user onboarding friction. It's hitting LTV to CAC ratio, and honestly, it might affect Monthly Recurring Revenue, right?


Look, we got serious latency issues in US East region, latency issues that are killing our MAU. If we can't stabilize this, then we could see... how should I put it... if we don't fix this now we could lose the product market fit for new AI feature. We're seeing churn increase in EMEA market, churn increase that is hitting like three point five percent. It is... it is worrying because our active user growth... our active user growth is slowing down. We need to check the server logs in the eastern region, I mean the US East region, and see if the load balancer is failing. We need to act fast or we lose the traction we built in the new system.

Look, we got serious latency issues in US East region, latency issues that are killing our MAU. If we can't stabilize this, then we could lose the product market fit for the new AI feature. We're seeing churn increase in EMEA market, churn increase that is hitting 3.5%. It is worrying because our active user growth is slowing down. We need to check the server logs in the US East region and see if the load balancer is failing. We need to act fast or we lose the traction we built in the new system.


So, uh, do we actually think this timeline is... I mean, is it realistic given where we are? I don't know, honestly, I'm kind of worried. Like, I was talking to Liam O'Connor and, uh, Alistair Cook, and it feels like we're hitting some major cross functional dependency bottlenecks. Specifically with the P ninety nine latency targets. I think Sanjay Gupta mentioned we're looking at, uh, four hundred milliseconds... oh no, sorry, five hundred milliseconds, which is way over budget. So, yeah, I just... I'm not sure we can hit the date without, you know, shifting some resources.

Do we actually think this timeline is realistic given where we are? I don't know, honestly, I'm worried. I was talking to Liam O'Connor and Alistair Cook, and it feels like we're hitting some major cross-functional dependency bottlenecks, specifically with the P99 latency targets. I think Sanjay Gupta mentioned we're looking at 500 milliseconds, which is way over budget. So, I'm not sure we can hit the date without shifting some resources.


Basically we're sticking with React for frontend, sticking with React because it just handles state better, and we use a PostgreSQL database, oh no, MongoDB for the backend, so we... I mean... if we look at the architecture, it's just more flexible, more flexible for our current scale of about five hundred users.

We're sticking with React for frontend because it just handles state better, and we use MongoDB for the backend. If we look at the architecture, it's just more flexible for our current scale of about 500 users.


So looking at the data, we saw a churn spike among enterprise tier users, a churn spike among enterprise tier users, right around mid March. It looks like we had some latency issues in the US East region, which probably hit the experience. Now, our Customer Acquisition Cost was around twelve dollars... oh no, it was fifteen dollars. If we can stabilize the platform performance, then we could probably get buy in from leadership to scale the spend. But we gotta be careful because the burn rate, the burn rate is currently way higher than we projected for this quarter.

So looking at the data, we saw a churn spike among enterprise tier users right around mid-March. It looks like we had some latency issues in the US-East region, which probably hit the experience. Now, our Customer Acquisition Cost was around $15. If we can stabilize the platform performance, then we could probably get buy-in from leadership to scale the spend. But we gotta be careful because the burn rate is currently way higher than we projected for this quarter.


Honestly, I know the team had some reservations, but if we look at the API uptime requirements, this is the only way to guarantee those nines. I mean, I've already started to socialize the proposal with Arjun Mehta and he's mostly on board, though we still need to tighten up the docs by the next steering committee. It's not like we're trying to invent something as radical as Satoshi Nakamoto did with Bitcoin or whatever, it's just... it's just about scalability. I think, uh, if we pivot now, we'll just be pushing the technical debt further down the road.

Honestly, I know the team had some reservations, but if we look at the API uptime requirements, this is the only way to guarantee those nines. I've already started to socialize the proposal with Arjun Mehta and he's mostly on board, though we still need to tighten up the docs by the next steering committee. It's not like we're trying to invent something as radical as Satoshi Nakamoto did with Bitcoin or whatever, it's just about scalability. I think if we pivot now, we'll just be pushing the technical debt further down the road.


Uh, just to be clear, it is not a database lock. Like, people think it is, but it's actually... uh... the issue is... wait, let me rephrase... we have a memory leak in the API gateway. It hit ninety eight percent usage at ten fifteen AM, and it's crashing the pods.

Just to be clear, it is not a database lock. People think it is, but we have a memory leak in the API gateway. It hit 98% usage at 10:15am, and it's crashing the pods.


Sarah, timeline... timeline looks... no, looks tight. Two weeks... oh no, three weeks, three weeks is not enough.

Sarah, timeline looks tight. 3 weeks is not enough.


I think it's more like... it's more like a quick fix than a long term solution. Like, the logic logic here is a bit messy. It's more like a patch than a real refactor. We should probably look at line eighty four eighty four again.

I think it's more like a quick fix than a long term solution. The logic here is a bit messy. It's more like a patch than a real refactor. We should probably look at line 84 again.


We add add the AI filter... no, AI search... search search for ten users.


Look, I think we should go with a microservices approach, just... because the latency is like ten milliseconds... oh no, it is twenty five milliseconds, and we really... if we just... I mean, if we decouple the data layer, we can scale it to five thousand concurrent users, right?

I think we should go with a microservices approach because the latency is 25 milliseconds, and if we decouple the data layer, we can scale it to 5,000 concurrent users, right?


So Sarah mentioned Sarah mentioned we should cut marketing by ten percent... wait, fifteen percent... no sorry, it is twenty percent! Anyway, the quarterly report... oh right, I think we need to double double the budget for RD because it's the only way we hit targets.

Sarah mentioned we should cut marketing by 20%! Anyway, the quarterly report... I think we need to double the budget for R&D because it's the only way we hit targets.


So uh look, we saw that conversion rate drop after v two point four release and I think... I mean... if we look at the API rate limiting thresholds, it's clear we need a change. Now, Amara Okafor suggested we could... uh... wait, let me rephrase that, we should actually scale the limits dynamically. I'm not talking about some crazy Satoshi Nakamoto level decentralization here, just basic load balancing. I want to get this fixed before Friday EOD so it doesn't affect the next sprint. Does that sound reasonable to you or do you think it'll take too long to implement in the current system? I mean we need to make sure the team can actually get it done by the end of the week so we don't have to delay the launch. But yeah, what's your take on the approach?

Look, we saw that conversion rate drop after v2.4 release and I think if we look at the API rate limiting thresholds, it's clear we need a change. Now, Amara Okafor suggested we should scale the limits dynamically. I'm not talking about some crazy Satoshi Nakamoto level decentralization here, just basic load balancing. I want to get this fixed before Friday EOD so it doesn't affect the next sprint. Does that sound reasonable to you or do you think it'll take too long to implement in the current system? We need to make sure the team can actually get it done by the end of the week so we don't have to delay the launch. But yeah, what's your take on the approach?


So, if we shift, shift about ten percent of the marketing budget, just... just ten percent, then we could, we could potentially scale our reach, reach more leads. I mean, it would really help us get to the next level, how should I say, it's definitely a win-win for the team, right?

If we shift about 10% of the marketing budget, then we could potentially scale our reach, reach more leads. It would really help us get to the next level; it's definitely a win win for the team, right?


He said two weeks. No, three weeks. Still too short, right?

He said 3 weeks. Still too short, right?


Change this, change this loop. It's too slow, too slow.


Uh first we launch in May... oh no, June. Then we we target ten thousand users, just to get the data data in. Finally, I think, it'll drive ten percent growth, right? Actually, it'll be a new star in the market.

First we launch in June.
Then we target 10,000 users, just to get the data in.
Finally, I think it'll drive 10% growth, right? Actually, it'll be a new star in the market.


So we saw user drop off at ten percent... no, twelve percent during onboarding. We really need to simplify simplify the flow. I think we should add add a one click sign in to boost conversion.

So we saw user drop off at 12% during onboarding. We really need to simplify the flow. I think we should add a one click sign in to boost conversion.


So, I'm not totally sure but I think, how should I put it, we might have missed the mark on some deliverables. I mean, I feel I feel like the friction between product and engineering was... it was just too high. We had like twelve... oh no, fourteen different blockers in the last sprint. And I think we need to need to rethink how we handle the hand off, you know, maybe set up some clearer guidelines for the next quarter, though I'm not completely certain if that solves the root cause, right?

I'm not totally sure but I think we might have missed the mark on some deliverables. I feel the friction between product and engineering was just too high. We had 14 different blockers in the last sprint. And I think we need to rethink how we handle the hand-off, maybe set up some clearer guidelines for the next quarter, though I'm not completely certain if that solves the root cause, right?


So, uh, for the backend, we are mostly using Node.js with a PostgreSQL database, and, well, we have integrated Redis for caching to keep things fast. I mean, it is basically the standard setup we have had since twenty twenty two. Oh, and we are currently migrating some of the legacy services over to Go, specifically the ones handling high concurrency, which should, you know, probably reduce our latency by about fifteen percent. I think it makes sense given our current scale, but, uh, I wanted to see, like, what your take is on that? Do you think we should stick with that roadmap or maybe look into something like Rust for those specific modules?

For the backend, we are mostly using Node.js with a PostgreSQL database, and we have integrated Redis for caching to keep things fast. It is the standard setup we have had since 2022. And we are currently migrating some of the legacy services over to Go, specifically the ones handling high concurrency, which should probably reduce our latency by about 15%. I think it makes sense given our current scale, but I wanted to see what your take is on that? Do you think we should stick with that roadmap or maybe look into something like Rust for those specific modules?


Bottom line is we need to cut cut the marketing spend by twelve percent. Twelve percent because... wait, fifteen percent. Oh no, sorry, it's actually ten percent. Since ROI is... ROI is falling, we should move fund to the new new product line, make sure we arrive at goal by December thirtieth. It's the only way to stay in budget for this year's fiscal year.

Bottom line is we need to cut the marketing spend by 10%. Since ROI is falling, we should move funds to the new product line, make sure we arrive at goal by December 30. It's the only way to stay in budget for this year's fiscal year.


So, if we shift ten percent... no, twelve percent of the marketing spend, then we could, uh, we could actually scale the growth, scale the growth in the APAC region. I mean, if we... if we reallocate those funds... wait, we could probably hit target by October twelfth, oh no, October fourteenth.

If we shift 12% of the marketing spend, then we could actually scale the growth in the APAC region. If we reallocate those funds, we could probably hit target by October 14.


Eh, Sarah said the churn rate hit twelve percent... no, it was fourteen percent. I think we need to look look at the feedback, just... how to put it... re examine the feedback patterns, you know?

Sarah said the churn rate hit 14%. I think we need to look at the feedback, re-examine the feedback patterns.


So uh looking at the conversion rates for variant B it seems like we actually saw a jump of about three point five percent which is great but then again when you look at the bounce rate it kind of... well it suggests that the new landing page might be a bit too cluttered for the mobile users so maybe we should try a third version for the launch on June first twenty twenty five to see if we can hit that five hundred twelve lead target

Looking at the conversion rates for variant B, it seems we saw a jump of about 3.5%, which is great, but then again when you look at the bounce rate, it suggests that the new landing page might be a bit too cluttered for the mobile users, so maybe we should try a third version for the launch on June 1, 2025 to see if we can hit that 512 lead target


So um, looking at the pipeline, I think we really need to focus on the senior dev roles for the marketing team... oh no, I mean the product team, since we got like, uh, four open headcounts right now and we're sort of... we're trailing behind on the... actually, let's just prioritize the backend stuff first because the current velocity is like, um, twelve percent... wait, no, fifteen percent lower than we planned for this quarter.

Looking at the pipeline, I think we really need to focus on the senior dev roles for the product team, since we got 4 open headcounts right now and we're trailing behind on the... actually, let's just prioritize the backend stuff first because the current velocity is 15% lower than we planned for this quarter.


I think some think we can finish by Friday... oh no, by next Tuesday. But we really need, we really need to scale back scope. We should... I mean... if we cut two features, we can actually hit that date, hit that date.

I think we can finish by next Tuesday. But we really need to scale back scope. If we cut 2 features, we can actually hit that date.


So, um, looking back at the last three months, if we just... if we actually shift our focus toward the Q four targets, then we could probably avoid the same bottlenecks we hit in August... oh no, I mean September, and maybe just streamline how the dev team and product sync up every Tuesday morning to keep things moving faster, you know?

Looking back at the last 3 months, if we shift our focus toward the Q4 targets, then we could probably avoid the same bottlenecks we hit in September, and maybe just streamline how the dev team and product sync up every Tuesday morning to keep things moving faster.


So, look, I think we all feel the weight of this quarter, and I know, I know the numbers were tough, like, we hit maybe forty two percent of target, but honestly, the main issue was... the main issue was the misalignment between product and engineering. We need to find a way to find a way to sync better, maybe start by reviewing the roadmap on October twelfth, or we could just... actually, let's just focus on the blockers first because the blockers are what's really killing our velocity right now.

I think we all feel the weight of this quarter, and I know the numbers were tough, we hit maybe 42% of target, but honestly, the main issue was the misalignment between product and engineering. We need to find a way to sync better, maybe start by reviewing the roadmap on October 12, or actually, let's just focus on the blockers first because the blockers are what's really killing our velocity right now.


Sarah, conversion rate rose ten percent... no, twelve percent. Twelve percent is a big win for the new page, the new page.

Sarah, conversion rate rose 12%. 12% is a big win for the new page.


Okay, bottom line is we gotta roll back the latest deploy. Uh, look, we seeing a spike in five hundred errors, like, maybe ten percent... oh no, it's actually twenty two percent of all traffic. So, we need to get this fixed fast before it hit the east... uh, winter region. I mean, the East region. Let's move.

Bottom line is we gotta roll back the latest deploy. We're seeing a spike in 500 errors, maybe 22% of all traffic. So, we need to get this fixed fast before it hits the East region. Let's move.


Look, we need to roll back now. Uh, the error rate is hitting five percent, and... I mean, if we... we should probably just revert to version two point zero four immediately.

Look, we need to roll back now. The error rate is hitting 5%, and we should probably just revert to v2.04 immediately.


Look, we've been looking at the current backlog and the team team is feeling a bit stretched, so we might need to push the launch to October twelfth... oh wait, October fourteenth. I think we think we should probably trim some features just to be safe, so let's aim for a soft launch by October twentieth instead, yeah?

Look, we've been looking at the current backlog and the team is feeling a bit stretched, so we might need to push the launch to October 14. I think we should probably trim some features just to be safe, so let's aim for a soft launch by October 20 instead, yeah?


Q3 loss was ten percent... no, twelve percent.


I'm not totally sure but I think... uh... if we push the date to October twelfth, we might... wait, let me rephrase... if we shift the deadline to October twelfth, we can probably hit those three main KPIs.

I'm not totally sure, but I think if we shift the deadline to October 12, we can probably hit those three main KPIs.


Honestly, the current sprint cycle, sprint cycle is just too slow. We need to move to two week cycles, two week cycles, to hit that ninety percent stability target by December twelfth.

Honestly, the current sprint cycle is just too slow. We need to move to 2-week cycles to hit that 90% stability target by December 12.


Actually, I think some people think this loop is causing the memory leak, but it is not, it is not the loop. The issue is actually the cache size... oh no, the cache limit, it is set to five hundred megabytes, which is way too high, so we should probably reduce it to two hundred fifty six megabytes, you know, just to be safe, right?

I think some people think this loop is causing the memory leak, but it is not the loop. The issue is the cache limit; it is set to 500MB, which is way too high, so we should probably reduce it to 256MB just to be safe, right?


Um, so the conversion rate hit twelve percent... oh no, it was fourteen point two percent, and we... just... the landing page... like, the bounce rate was... wait, let me just say, it actually performed better, right?

The conversion rate hit 14.2%, and the landing page bounce rate actually performed better, right?


预算能加点吗？十万，不对，十五万，我觉得行。

预算能加点吗？15万，我觉得行。


我我我今年KPI达成率是百分之一百一十二，嗯，其实应该是百分之一百一十五，就是我想想啊，整体超额完成了一点。

我今年KPI达成率是115%，整体超额完成了一点。


So uh look, I know last quarter was... it was rough, really rough for everyone. We had those server outages and, like, the team burnout... um, wait, the budget cut was... ten percent... no, sorry, twelve percent... wait, that's wrong, it was fifteen percent! And uh, the timeline... that was a mess, I think. I mean, the communication... uh, actually, the real issue we gotta tackle today is how we handle the handoff between product and engineering.

Look, I know last quarter was rough, really rough for everyone. We had those server outages and the team burnout. The budget cut was 15%! And the timeline was a mess, I think. The real issue we gotta tackle today is how we handle the handoff between product and engineering.


Uh, I think we should, well, maybe add one click checkout, and then, how should I put it, just, like, a loyalty system, plus, oh, we could get ten percent more users, ya, in the new market, right, ba.

I think we should add one-click checkout, a loyalty system, plus we could get 10% more users in the new market.


Look, for the phase one deadline, it's more like... we are polishing existing features than building new ones, just to hit October twelfth. I think we... let's say, it's better to trim scope now than risk a delay, you know?

For the phase 1 deadline, we are polishing existing features than building new ones, just to hit October 12. I think it's better to trim scope now than risk a delay.


简单来说，昨天那个接口跑了五百次，呃，结果…怎么说呢，其实整体成功率在百分之九十八左右，得在下周二前把这块新逻辑给对齐。

简单来说，昨天那个接口跑了500次，结果整体成功率在98%左右，得在下周二前把这块新逻辑给对齐。


老大说流程得简化，嗯，我觉得……其实应该把……怎么说呢，直接在星周报里同步，吧，这样能省掉两三个环节呀。

老大说流程得简化，我觉得其实应该直接在星周报里同步，这样能省掉2-3个环节呀。


嗯，咱们之前聊过上半年投放转化率才百分之三点五，就是得再增加五万块预算，怎么说呢，不然新项目很难跑通吧。

咱们之前聊过上半年投放转化率才3.5%，得再增加50000元预算，不然新项目很难跑通吧。


嗯，与其死磕功能迭代，不如先把转化率提高到百分之十二，啊不对，百分之十五吧，这比再死等新用户更有意义哈。

与其死磕功能迭代，不如先把转化率提高到15%，这比再死等新用户更有意义哈。


先是看那个，那那那个DAU，增长了百分之五，不对，百分之八，然后是留存率稍微掉了点，最后整体转化大概百分之十二。

先是看DAU增长了8%，然后是留存率稍微掉了点，最后整体转化大概12%。


刚才小李说预算得控制在五万，啊不对，是六万。我觉得预算预算得再加一点，怎么说呢，增加十个百分点吧，方案整体方案整体才稳妥哈。

刚才小李说预算得控制在6万。我觉得预算得再加一点，增加10%吧，整体方案才稳妥哈。


嗯，老大之前说咱们组现在只有五个人，哦不是，应该是六个。怎么说呢，我我我觉得这规模其实挺挺合适的，就是能保证效率吧。

老大之前说咱们组现在应该是6个人。我觉得这规模其实挺合适的，能保证效率吧。


下个月市场波动挺大，咱们那个目标…嗯…怎么说呢…得定在五十万，不对，六十万，等等错了，是八十万！

下个月市场波动挺大，咱们目标得定在80万！


那个，你可能觉得，得先写完文档才，嗯，不是那样，其实是先出个 Demo，大概，得，三五个版本，迭代出来再写。

你可能觉得得先写完文档，其实是先出个Demo，大概得3-5个版本迭代出来再写。


嗯，我看方案里预算写了三万块，啊不对，是五万块，就是成本有点高吧，你觉得呢？

我看方案里预算写了5万元，成本有点高吧，你觉得呢？


如果咱们能把转化率转化率提高到百分之五，提高到百分之五，那我们在下个季度下个季度得能多赚三百万，到那个时候再考虑扩充团队。

如果咱们能把转化率提高到5%，那我们在下个季度得能多赚300万元，到那个时候再考虑扩充团队。


嗯，大家可能觉得这次转化率掉是因为投放渠道问题，其实不是，怎么说呢，主要还是产品端某个 Bug 导致，我看数据是百分之十二点五，啊不对，是百分之八点二，这个波动导致我们整体 KPI 没达标吧。

大家可能觉得这次转化率掉是因为投放渠道问题，其实不是，主要还是产品端某个 Bug 导致，我看数据是 8.2%，这个波动导致我们整体 KPI 没达标吧。


说白了，嗯，我们组现在就是主攻主攻那个底层架构，怎么说呢，目前大概有十二个人，十二个人在做，整体节奏挺快吧，再之后我们会往新方向走哈。

说白了，我们组现在主攻底层架构，目前大概有12个人在做，整体节奏挺快吧，之后我们会往新方向走哈。


其实这事不是单纯 链路问题，嗯，其实是，怎么说呢，核心在于那个接口权限，大概有百分之十二…不对，百分之十八 接口没对齐，导致整体风控延迟了。

其实这事不是单纯链路问题，核心在于接口权限，大概有18%接口没对齐，导致整体风控延迟了。


我想想啊，如果今年我们能把那个转化率提升到百分之十二，啊不对，是百分之十五吧，那我们明年大概能，怎么说呢，争取把整体GMV拉高到五千万左右哈。

如果今年我们能把转化率提升到15%，那我们明年大概能争取把整体GMV拉高到5000万左右。


结论百分之十，啊不对，百分之十二，主要因为咱们那个新渠道转化率涨快了。

结论12%，主要因为咱们新渠道转化率涨快了。


哦对了，那个，截止日期...应该是，呃，下周三，不对，二零二四年十月十八号，大家记得，那个，及时把PPT，上传到Drive，千万别...忘了。

截止日期应该是2024年10月18日，大家记得及时把PPT上传到Drive，千万别忘了。


刚才王总提到提到市场饱和，我觉得我觉得这个观点不对，目前份额只有百分之十二，啊不对，百分之十五，还得再深挖深挖，才能到目标点。

刚才王总提到市场饱和，我觉得这个观点不对，目前份额只有15%，还得再深挖，才能到目标点。


哦对了，嗯，这次转化率转化率得达到百分之十五，怎么说呢，这个指标指标得在二零二五年三月前完成吧。

这次转化率得达到15%，这个指标得在2025年3月前完成吧。


咱们这次转化率转化率到了百分之十二，啊不对，百分之十四，这个增长增长势头挺好。

咱们这次转化率到了14%，增长势头挺好。


哦对了，嗯，交付进度目前在百分之八十，啊不，百分之八十五，就是，我们那个...怎么说呢...整体跑通了，在验收阶段哈。

交付进度目前在85%，整体跑通了，在验收阶段。


嗯，先是去年六月，我们团队才三个人，然后，怎么说呢，规模在，扩到了十来个，最后，诶，现在的架构应该是分了三个小组吧。

去年6月，我们团队才3个人，然后规模扩到了10来个，最后现在的架构应该是分了3个小组吧。


嗯，不是说项目延期，其实其实是进度调整，怎么说呢，原定百分之八十，我想想，其实是达到了百分之九十五，吧。

不是说项目延期，其实是进度调整，原定80%，其实是达到了95%。


王总，人力资源人力资源得调一下，大概得要三个人三个人，不对，两个就行，下周二下午两点半开会讨论

王总，人力资源得调一下，大概要2个人就行，下周二14:30开会讨论


你说咱们这个PR流程能不能再快点，嗯，我想想啊，之前一个需求跑了三四天，其实只要把代码提交到Git以后，等一个小时左右审批完就行，就是这样吧。

你说咱们这个PR流程能不能再快点，之前一个需求跑了3-4天，其实只要把代码提交到Git以后，等1小时左右审批完就行。


大家注意一下进度，先是需要在6月1号前完成初稿，然后是6月15号前把审核流程走完，最后在6月30号前正式提交，希望能严格把控时间节点。

大家注意一下进度：
1. 6月1日前完成初稿
2. 6月15日前把审核流程走完
3. 6月30日前正式提交
希望能严格把控时间节点。


嗯，咱们组平时同步进度就是，每周一上午十点，啊不对，是十点半开周会，然后...那个流程应该是...怎么说呢，大家把文档填好传到共享盘吧，你觉得这样操作行吗呀？

咱们组平时同步进度，每周一10:30开周会，流程应该是大家把文档填好传到共享盘吧，你觉得这样操作行吗？


嗯，目前ROI大概…就是…百分之十五吧，诶，你觉得呢哈？

目前ROI大概15%，你觉得呢？


我想想啊，与其盯着百分之三点五这个转化率，不如看活跃用户比上个月增长了五百一十二个，这更关键吧。

与其盯着3.5%这个转化率，不如看活跃用户比上个月增长了512个，这更关键吧。


这个季度增长率大概百分之十二，额，其实说得更准点，也就是百分之十二点三，基本达标。

这个季度增长率大概12%，其实说得更准点，也就是12.3%，基本达标。


先是把那个API接通，然后…其实应该是先调接口，最后再把这三个页面上线，估计得花五天，得赶紧弄完。

先调接口，最后把这三个页面上线，估计得花5天，得赶紧弄完。


与其投广告投广告，不如投人投人，预算得三万，不对，五万。

与其投广告，不如投人，预算得5万。


嗯，之前看市场规模，市场规模大概有三千万，啊不对，是五千万，怎么说呢，我想想啊，其实核心核心在于这个 ROI 能够快速跑通吧。

之前看市场规模大概有5000万，其实核心在于这个ROI能够快速跑通吧。


老王，我想想啊，咱们这次用三台服务器，哦不是，五台，得保证在高峰期能扛住。

老王，咱们这次用5台服务器，得保证在高峰期能扛住。


诶，去年KPI达成率百分之八十，哦不是，百分之九十二，嗯，这个进度…我想想啊…其实进度比预期快，就是挺稳吧？

去年KPI达成率92%，进度比预期快，挺稳吧？


其实不是说进度慢，嗯，是那个…风险点在…其实是逻辑漏洞，可能导致百分之五得损耗，得再关注下。

其实不是说进度慢，是风险点在逻辑漏洞，可能导致5%的损耗，得再关注下。


诶，其实留存率不是在下降，怎么说呢，整体趋势是在回升。我想想啊，上周大概百分之十二，不对，百分之十五，回升回升了百分之十五吧。

其实留存率不是在下降，整体趋势是在回升。上周大概回升了15%吧。


哦对了，那个合规风险点，大概有百分之三点五，得在二零二四年十二月一号前搞定，这个新规定...呃，得赶紧再确认一下，对，是对的。

合规风险点大概有3.5%，得在2024年12月1日前搞定，这个新规定得赶紧再确认一下。


嗯，刚才老王说用 Redis 缓存，就是我觉得响应时间得压到两百毫秒，哦不是，应该是五十毫秒，怎么说呢，这样用户体验才稳吧。

刚才老王说用 Redis 缓存，我觉得响应时间得压到 50 毫秒，这样用户体验才稳吧。


嗯，与其死磕这百分之五，就是，比追求量，得更看重客单价吧。

与其死磕这5%，比追求量，得更看重客单价吧。


老王，那个需求文档需求文档得赶紧出，周五周五下午三点半之前得交，不然进度进度会卡住。

老王，需求文档得赶紧出，周五15:30之前得交，不然进度会卡住。


咱们人力成本预计增加百分之十，啊不对，百分之十五，人力成本得覆盖。那个招聘渠道，对了，能不能给咱们额外拨两万块

咱们人力成本预计增加15%，人力成本得覆盖。招聘渠道能不能给咱们额外拨20000元


嗯，如果这波流量能维持在两千五百左右，两千五百的话，那咱们转化率哈，估计能到百分之五，百分之五吧。

如果这波流量能维持在2500左右，那咱们转化率估计能到5%。


关于这篇论文的第三章，我认为在讨论 Transformer 模型时，应该更详细地分析自注意力机制的计算开销。


关于第三季度市场分析报告，我认为我们需要在用户留存率这一块增加更多的数据对比图表。


关于这篇论文的第三章，我认为在分析对比实验结果时，需要引入更多的消融实验来验证该模块的有效性。


最近压力有点大，感觉需要找个周末去山里徒步放松一下，你有兴趣一起去吗？

最近压力有点大，感觉需要找个周末去山里徒步放松一下，你有兴趣一起去吗？


备忘：明天上午 9 点开周会，记得带上上季度的销售数据分析报告。

备忘：明天上午 9 点开周会，记得带上上季度的销售数据分析报告。


今天下午 3 点的周会重点讨论 Q3 的预算分配以及新产品的上线计划，请大家提前准备好各自的汇报 PPT。


帮我把下午3点在会议室B的周会推迟到4点，并通知所有参会人员。

帮我把下午3点在会议室B的周会推迟到4点，并通知所有参会人员。


记得明天早上8点提醒我给客户发邮件，附件要加上最新的报价单和产品规格书。

记得明天早上8点提醒我给客户发邮件，附件要加上最新的报价单和产品规格书。


最近压力有点大，感觉每天都在重复同样的事情，好想找个周末去山里徒步放松一下。

最近压力有点大，感觉每天都在重复同样的事情，好想找个周末去山里徒步放松一下。


我想搜一下2024年最受欢迎的开源深度学习框架对比分析。

我想搜一下2024年最受欢迎的开源深度学习框架对比分析。


我想在 2025 年的第三季度完成这个项目的初步调研，重点分析用户留存率和转化率。


请帮我导航到上海市浦东新区世纪大道 1 号，避开拥堵路段。

请帮我导航到上海市浦东新区世纪大道 1 号，避开拥堵路段。


帮我把明天下午 3点 的会议提醒改成 3点半，顺便在日程表里加上准备 PPT 的事项。

帮我把明天下午 3:00 的会议提醒改成 3:30，顺便在日程表里加上准备 PPT 的事项。


帮我记一下：明天下午 2 点记得给客户发邮件，确认最终的合同条款。

帮我记一下：明天下午 2 点记得给客户发邮件，确认最终的合同条款。


我想搜索一下 2024 年最受欢迎的开源大语言模型对比分析报告。

我想搜索一下 2024 年最受欢迎的开源大语言模型对比分析报告。


提醒我明天早上 8 点记得带上笔记本电脑和充电线。

提醒我明天早上 8 点记得带上笔记本电脑和充电线。


请问从现在的位置到上海图书馆最快的路线是什么？预计需要多久？

请问从现在的位置到上海图书馆最快的路线是什么？预计需要多久？


请帮我把这段 Python 代码中的循环逻辑优化一下，现在的运行时间太长了。

请帮我把这段 Python 代码中的循环逻辑优化一下，现在的运行时间太长了。


这次季度汇报的重点应该放在用户增长率和获客成本的优化上，尤其是 20-30 岁这个年龄段。


请导航到上海市静安区南京西路 1618 号，避开拥堵路段。

请导航到上海市静安区南京西路 1618 号，避开拥堵路段。


周六晚上 7 点在三里屯见，我们去吃那家新开的泰国餐厅吧。

周六晚上 7 点在三里屯见，我们去吃那家新开的泰国餐厅吧。


最近读的那本书非常有启发，尤其是关于时间管理的那一部分，让我重新思考了工作优先级。


最近在阅读关于 Transformer 架构的论文，特别是关于注意力机制在长文本处理上的效率优化问题。


请将这段 Python 代码中的循环逻辑优化一下，尽量降低时间复杂度，避免使用嵌套循环。


关于深度学习在医学影像分析中的应用，我认为目前的注意力机制在处理长距离依赖时仍有提升空间。


你好，我的订单号是 88273615，我想咨询一下包裹目前到哪个转运中心了？

你好，我的订单号是 88273615，我想咨询一下包裹目前到哪个转运中心了？


请帮我搜索一下 Python 中如何使用异步 IO 提高大规模网络请求的并发性能。


帮我订一张明天下午 2 点从上海到北京的高铁二等座机票。

帮我订一张明天下午 2 点从上海到北京的高铁二等座机票。


帮我记录一条备忘录：记得在周三下午 3 点预约牙医，并带上之前的 X 光片。

帮我记录一条备忘录：记得在周三下午 3 点预约牙医，并带上之前的 X 光片。


今天心情不错，打算周末约几个好朋友一起去郊区露营，顺便拍一些照片。

今天心情不错，打算周末约几个好朋友一起去郊区露营，顺便拍一些照片。


将这个函数重构为异步模式，并确保在调用 API 时添加 500 毫秒的超时处理。


关于深度学习中的梯度消失问题，我们应该讨论一下使用 ReLU 激活函数与 LSTM 结构的对比。


在深度学习模型中，通过引入注意力机制可以有效解决长序列依赖问题，从而提升Transformer架构的性能。


关于第二季度的市场推广计划，我认为我们需要增加在社交媒体端的投入，重点针对 18 到 25 岁的用户群。


明天下午3点你有空吗？我想约你一起去市中心那家新开的咖啡馆坐坐。

明天下午3点你有空吗？我想约你一起去市中心那家新开的咖啡馆坐坐。


帮我把今天的待办事项记录下来：下午2点开周会，4点回复客户邮件，晚上记得给猫喂食。


我想查询一下 2024 年巴黎奥运会开幕式的具体时间。

我想查询一下 2024 年巴黎奥运会开幕式的具体时间。


我的订单号是 20240812993，我想申请办理退货，请问具体的流程是怎么样的？


关于第二季度的市场分析报告，我认为我们需要重点关注用户留存率的下滑原因。

关于第二季度的市场分析报告，我认为我们需要重点关注用户留存率的下滑原因。


关于大语言模型在医疗诊断中的鲁棒性问题，我认为我们需要引入更多高质量的对抗性样本进行微调。


请将当前项目的所有 API 接口文档同步到 Confluence 页面，并通知前端开发团队。


关于这个季度的人员绩效考核，我认为应该增加一个关于团队协作的量化指标。

关于这个季度的人员绩效考核，我认为应该增加一个关于团队协作的量化指标。


我想查一下订单号 20240512998 的快递到哪了，为什么显示在上海停了 3 天？


我们需要在模型训练中引入 L2 正则化，以防止在验证集上出现过拟合现象。

我们需要在模型训练中引入 L2 正则化，以防止在验证集上出现过拟合现象。


关于这篇论文的第三章，我认为目前的消融实验不足以支撑结论，需要增加对比基准模型。


最近压力有点大，想在这个周末去郊外徒步放松一下，你有兴趣一起去吗？

最近压力有点大，想在这个周末去郊外徒步放松一下，你有兴趣一起去吗？


最近在看一本关于量子力学的书，感觉里面的波函数概念非常有趣。

最近在看一本关于量子力学的书，感觉里面的波函数概念非常有趣。


帮我查一下去上海虹桥机场最快的路线，现在堵车严重吗？

帮我查一下去上海虹桥机场最快的路线，现在堵车严重吗？


在 Transformer 架构中，多头注意力机制能够让模型在不同表示子空间中共同学习信息。


关于这篇论文的第三章，我认为在实验部分需要增加更多关于消融研究的对比数据，以增强结论的说服力。


我想咨询一下订单号 20240512998 的物流状态，显示已经到达分拨中心三天了，请帮我核实一下。


帮我订一张明天上午 10 点去上海的高铁二等座票。

帮我订一张明天上午 10 点去上海的高铁二等座票。


记得在购物清单里加上 2 升牛奶、一打鸡蛋和一袋全麦面包。

记得在购物清单里加上 2 升牛奶、一打鸡蛋和一袋全麦面包。


关于第二季度的销售报告，我建议在 PPT 中增加一个对比图表，详细分析 5 月份的增长原因。


最近在读的一本书非常有启发，它讨论了认知心理学在现代产品设计中的实际应用。

最近在读的一本书非常有启发，它讨论了认知心理学在现代产品设计中的实际应用。


关于下个季度的市场推广计划，我认为我们应该增加在短视频平台的投放预算，以吸引更多 18 到 25 岁的年轻用户。


记得买牛奶、鸡蛋和洗洁精，顺便问一下妈妈周末要不要一起去公园野餐。

记得买牛奶、鸡蛋和洗洁精，顺便问一下妈妈周末要不要一起去公园野餐。


在 Python 中，如何使用 pandas 库实现对一个 2GB 大小的 CSV 文件进行分块读取？


关于第二季度的营收增长，我们目前的初步分析显示，主要驱动力来自于东南亚市场的 B2C 业务扩张。


周末有空吗？我们一起去那家新开的日料店试试吧，听说他们的Omakase性价比很高。


关于这个季度的KPI，我认为我们在用户留存率方面还需要进一步优化，建议增加针对新用户的引导流程。


记得明天早上 8 点去超市买牛奶、鸡蛋和 2 公斤苹果。

记得明天早上 8 点去超市买牛奶、鸡蛋和 2 公斤苹果。


帮我记录一条备忘录：记得在周五下班前把项目进度表发给王经理。

帮我记录一条备忘录：记得在周五下班前把项目进度表发给王经理。


帮我预定明天下午 2 点的会议室，记得邀请产品组的所有成员。

帮我预定明天下午 2 点的会议室，记得邀请产品组的所有成员。


记得在 2025 年 3 月 15 日之前提交所有的报销凭证，否则无法在当月结算。


最近心情挺不错的，感觉生活节奏慢了下来，打算周末去郊区徒步放松一下。

最近心情挺不错的，感觉生活节奏慢了下来，打算周末去郊区徒步放松一下。


我们在部署 K8s 集群时遇到了 Pod 调度问题，需要检查一下节点资源配额和 Taints 设置。


关于这篇论文的第三章，我认为目前的消融实验不足以证明该模块的有效性，需要增加对比基准。


提醒我明天上午 10 点给王经理发邮件，把上周的销售报告附件加上。

提醒我明天上午 10 点给王经理发邮件，把上周的销售报告附件加上。


我想在 Python 中使用 FastAPI 构建一个异步的 REST API，应该如何配置中间件？


我想咨询一下订单号 2024051299 的物流状态，为什么已经在转运中心停留 3 天了？


周末有空吗？我们要不要一起去那家新开的 brunch 餐厅试试，听说他们的班尼迪克蛋很出名。


在 Python 中，如何使用 asyncio 库来并发处理多个 HTTP 请求以提高爬虫的效率？


关于这个分布式系统的共识算法，我认为 Raft 比 Paxos 更容易在实际工程中实现，因为它的领导者选举机制更清晰。


帮我写个接口，嗯，与其用 Zustand 存状态，不如直接在 Nuxt 后端处理，感觉比 Tiptap 传参要快得多，我想用 LlamaIndex 挂个索引，哦不对，应该是用 Tauri 端的本地存储，这样性能更强吧。

帮我写个接口，与其用 Zustand 存状态，不如直接在 Nuxt 后端处理，感觉比 Tiptap 传参要快得多，我想用 Tauri 端的本地存储，这样性能更强吧。


那个，如果咱们用 Tauri 把这个前端给包起来，然后把 hidden_dim 设成五百一十二，怎么说呢，要是咱们能把 Keras 那套逻辑迁移到 Julia 里面去，那我们就能把 alpha 参数调得更灵活一些。

如果咱们用 Tauri 把前端给包起来，然后把 hidden_dim 设成 512，要是咱们能把 Keras 那套逻辑迁移到 Julia 里面去，那我们就能把 alpha 参数调得更灵活一些。


诶，你说这个 cache ttl 设置成六十秒会不会太短了？我想想啊，我现在是用 Julia 调 Scala 接口，结果发现 cache ttl 缓存失效太快，太快了，怎么说呢，我怀疑 Lua 脚本那边逻辑有问题，有问题，应该是把 TTL 读成毫秒吧。

你说这个 cache_ttl 设置成60秒会不会太短了？我想想，我现在是用 Julia 调 Scala 接口，结果发现 cache_ttl 缓存失效太快，我怀疑 Lua 脚本那边逻辑有问题，应该是把 TTL 读成毫秒吧。


诶，我现在在用 Tauri 搞一个 Clojure 写的后端，就是这个 Astro 框架那边，Astro 框架那边好像有个 ConnectionTimeout 问题，我想想啊，帮我写个单元测试，测试一下 compression level 设成九，不对，设成五的时候能不能正常跑通吧。

我现在在用 Tauri 搞一个 Clojure 写的后端，Astro 框架那边好像有个 ConnectionTimeout 问题，帮我写个单元测试，测试一下 compression_level 设成 5 的时候能不能正常跑通吧。


哎呀太夸张了，嗯，这段代码竟然在用 Elixir 处理数据，哦不是，是 Julia，怎么说呢，我明明配置了 Prisma 但现在一直报 ConnectionRefused，而且还跳出个 NullPointerException，你帮我看看怎么回事吧。

太夸张了，这段代码竟然在用 Julia 处理数据，我明明配置了 Prisma 但现在一直报 ConnectionRefused，而且还跳出个 NullPointerException，你帮我看看怎么回事吧。


诶那个，与其用 Scala 这种，额，不如直接用 Haskell 重构，比那个，怎么说呢，我想想，比用 Lua 写的逻辑更函数式一点，你帮我把这块代码在，再优化下，用 FastHTML 那个框架，对，FastHTML，得把接口改掉

与其用 Scala，不如直接用 Haskell 重构，比用 Lua 写的逻辑更函数式一点，你帮我把这块代码再优化下，用 FastHTML 框架，得把接口改掉


我想写个 regex 匹配 Knative 那个 config 里的 lora alpha 参数, 应该是用 Scala 写, 啊不对, 是用 Lua 写, 这样能直接在 runtime 跑, 你觉得呢?

我想写个 regex 匹配 Knative config 里的 lora_alpha 参数，应该是用 Lua 写，这样能直接在 runtime 跑，你觉得呢？


嗯，先是我想在 HuggingFace 上拉个模型，然后用 Julia 跑了一下 beta2 版本，就是 compression ratio 设成了百分之零点五，不对，应该是百分之零点八，最后在 Kotlin 这一层报错了呀。

我想在 HuggingFace 上拉个模型，然后用 Julia 跑了一下 beta2 版本，compression_ratio 设成了 0.8%，最后在 Kotlin 这一层报错了。


对了，我觉得我觉得这个 Drizzle 适配得不太好，嗯，要是把……怎么说呢，把 compression ratio 调到零点五，不对，是零点八，应该能优化性能，话说咱们这个 Mistral 模型，模型是用 Xformers 跑，跑在 Prisma 上吧？

我觉得 Drizzle 适配得不太好，要是把 compression_ratio 调到 0.8，应该能优化性能。话说咱们这个 Mistral 模型是用 Xformers 跑在 Prisma 上吧？


诶，你说把这个 Elixir 逻辑迁移到 Zig 能不能解决内存碎片问题？我想想啊，我现在的内存占用到了八百兆，不对，是六百四十兆，我觉得这个迁移，迁移到 Zig 应该能优化，能优化掉很多冗余，反正 Vercel 那边部署也支持，应该可以吧。

你说把 Elixir 逻辑迁移到 Zig 能不能解决内存碎片问题？我现在的内存占用到了640MB，我觉得迁移到 Zig 应该能优化掉很多冗余，反正 Vercel 那边部署也支持，应该可以吧。


嗯，如果 timeout ms 设成五千毫秒的话，那我们就能排除掉，排除掉网络抖动问题，我想想啊，要是后端用 Scala 写的…不对，应该是用 Haskell 写的，那逻辑层可能哈，可能就卡在内存回收那块吧。

如果 timeout_ms 设成5000毫秒的话，那我们就能排除掉网络抖动问题。要是后端用 Haskell 写的，那逻辑层可能就卡在内存回收那块吧。


哦对了,帮我写个正则表达式,要把 DeepSpeed 里面那段 Pydantic 校验逻辑,校验逻辑给提取出来,对了,要把那个 Olive 框架, Olive 框架里定义的一百二十八位长度字符串给匹配到,哎不对,应该是两百五十六位,你得帮我兼容下 Xformers 那个配置项,配置项得支持 Elixir 这种格式,懂我意思吧?

帮我写个正则表达式，要把 DeepSpeed 里面那段 Pydantic 校验逻辑提取出来，要把 Olive 框架里定义的 256 位长度字符串给匹配到，你得帮我兼容下 Xformers 那个配置项，配置项得支持 Elixir 这种格式，懂我意思吧？


嗯, 如果这块内存泄漏在 Zustand 状态管理里, 那我们就能快速定位, 怎么说呢, 只要把 tRPC 的那个接口超时时间改成五秒, 哦不是, 三秒, 应该就能排查出问题吧。

如果这块内存泄漏在 Zustand 状态管理里，那我们就能快速定位，只要把 tRPC 的接口超时时间改成3秒，应该就能排查出问题吧。


嗯，帮我把这个 query 换成 index seek 吧，怎么说呢，现在这个 query plan 跑出来是 index scan，导致在三千五百毫秒左右才返回，得优化一下得太慢了哈。

帮我把这个 query 换成 index seek 吧，现在这个 query plan 跑出来是 index scan，导致在 3500 毫秒左右才返回，得优化一下，太慢了。


你说用 Ollama 跑那个 Olive 优化过模型能不能配 Drizzle？嗯，我想想，把 lora rank 设成六十四，batch_size 调到三十二，不对，应该是十六，应该没问题吧。

你说用 Ollama 跑 Olive 优化过模型能不能配 Drizzle？我想想，把 lora_rank 设成64，batch_size 调到16，应该没问题吧。


助手，我想在用 Dart 和 XState 写的状态机里优化下 Drizzle 的查询，现在频繁报 IndexOutOfBounds 还有 ConnectionTimeout，帮我看看怎么调。


帮我把这段 Python 代码，翻译成 Java，哦不是，是 Go 语言，把那个 list 转换成 slice 就行。

帮我把这段 Python 代码翻译成 Go 语言，把 list 转换成 slice 就行。


嗯，你帮我把 Docker 镜像重新打一遍，因为我想想啊，我想想啊，我得把 Dart 加上 Lua 还有 Solidity 全给打包进去，就是，为了确保环境一致，那个……把 Ollama 端口改成，改成一万一千零一号，诶，对了，应该是十一千一百一十一号吧。

你帮我把 Docker 镜像重新打一遍，我得把 Dart 加上 Lua 还有 Solidity 全给打包进去，为了确保环境一致，把 Ollama 端口改成 11111 号。


嗯，之前写的那个 Modal 组件，Modal 组件逻辑有点乱，怎么说呢，我想把 z index 改成九百九十九，改成九百九十九吧。

之前写的那个 Modal 组件逻辑有点乱，我想把 z-index 改成 999。


嗯，我现在的索引索引优化是这样，把那个 B tree 换成 Hash 索引，哦不是，应该是覆盖索引，覆盖索引这样能快一点吧，你觉得呢？

我现在的索引优化是这样，把 B-tree 换成覆盖索引，这样能快一点吧，你觉得呢？


对了，那个…把…把 batch size 改成八


帮我写个数据迁移脚本，先是把旧库里版本一点零的数据全部导出，导出到临时表，然后检查一下字段，只要状态不等于零且等级大于等于五的记录才迁移，迁移这些记录，最后用C加加写个校验逻辑，调用那个双冒号后面的静态方法核对一遍，核对一遍总数对不对。

帮我写个数据迁移脚本：
1. 把旧库里v1.0的数据全部导出到临时表
2. 检查一下字段，只要状态!=0且等级>=5的记录才迁移
3. 用C++写个校验逻辑，调用双冒号后面的静态方法核对一遍总数对不对


对了，帮我写个 Regex 匹配那个 email，嗯，长度得大于等于十个字符，不对，应该是八个字符吧，得支持那种带点号的域名。

帮我写个 Regex 匹配 email，长度得 >= 8 个字符，得支持那种带点号的域名。


写个v一点二，不对，v一点三 API？行吧。


我那个Docker镜像现在在跑v一点二，啊不对，是v一点五，我想把那个...呃...怎么说呢，把端口映射改成八千零八十二，不对，是八千零八十

我Docker镜像现在在跑v1.5，我想把端口映射改成8080


正则正则…写个正则…匹配不等于零，你觉得呢？

写个正则匹配!=0，你觉得呢？


你帮我弄下CI CD流水线，先是触发Git push，触发Git push之后跑单元测试，然后把镜像推到Docker hub，推到Docker hub，最后部署到k8s集群。

你帮我弄下CI/CD流水线：
1. 触发Git push
2. 跑单元测试
3. 把镜像推到Docker Hub
4. 部署到K8s集群


说白了，我想想啊，这段逻辑逻辑太冗余，帮我把这个 loop 循环，循环改成 map 映射，把时间复杂度降到 O 1。

说白了，这段逻辑太冗余，帮我把这个 loop 循环改成 map 映射，把时间复杂度降到 O(1)。


哦对了，帮我看看这段C加加代码在干嘛，那个if判断里是不是写成x大于等于五了，不对，应该是感叹号等于零，那个箭头指向哪个member我不清楚。

帮我看看这段C++代码在干嘛，if判断里是不是写成x!=0了，箭头指向哪个member我不清楚。


论文里说要把 learning rate 设成 0.01，不对，是 0.001。我觉得这太保守，咱们能不能...呃...怎么说呢...直接用 C加加 写个 loop，判断 loss 大于等于 0.5 就跳出

论文里说要把 learning rate 设成 0.001。我觉得这太保守，咱们能不能直接用 C++ 写个 loop，判断 loss >= 0.5 就跳出


说白了，帮我写个 regex，匹配那个长度大于五位，不对，大于八位，且不包含感叹号等于号 这种特殊字符 字符串

说白了，帮我写个regex，匹配长度大于8位，且不包含!=这种特殊字符的字符串


嗯，如果把这个 API 接口改成 POST，啊不，是 PUT，那我们就能，就是，直接更新数据库里那三千五百条记录吧。

如果把这个API接口改成PUT，那我们就能直接更新数据库里那3500条记录吧。


我也不太确定，但是感觉把 batch size 改成 512 之后，训练速度能快 百分之三点五 左右。

我也不太确定，但是感觉把 batch size 改成 512 之后，训练速度能快 3.5% 左右。


嗯，组长说这个接口得覆盖边界值，我觉得我觉得得写个 unit test，就是把输入设成十个，哦不是，设成一百个吧，哈。

组长说这个接口得覆盖边界值，我觉得得写个 unit test，把输入设成100个。


把这个，把这个 Loop, 把 Loop 抽出来, 怎么说, 抽成函数, 你觉得呢？

把Loop抽成函数，你觉得呢？


哦对了，我想给那个模块加个 cache 机制，就是把所有大于等于五百个元素 列表存到 Redis 里，诶，不对，应该是大于等于五百一十二个，然后用双冒号调用那个 Singleton 类，哈。

我想给那个模块加个 cache 机制，把所有 >= 512 个元素的列表存到 Redis 里，然后用 :: 调用 Singleton 类。


帮我看看这段C加加代码，看看这段C加加代码在干嘛，然后这里有个指针箭头指向 member 变量，还有这个 double 类型的 threshold 阈值阈值怎么设，另外判断 if 语句里那个不等于零逻辑对不对

帮我看看这段C++代码在干嘛，然后这里有个指针->指向member变量，还有这个double类型的threshold阈值怎么设，另外判断if语句里那个!=0逻辑对不对


对了，帮我 check 一下那个 deployment 配置，我觉得我觉得那个内存限制设得太低，得把 memory limit 调高点。那个值设成四 G…不对，八 G…等等错了，是十六 G！话说咱们那个…要是把…怎么说呢…如果把 C plus plus 编译器版本换成十四号的话，会不会导致 binary 兼容性问题？你检查检查这个版本，检查检查一下环境变量里有没有那个 Path 路径。

帮我check一下deployment配置，我觉得内存限制设得太低，得把memory limit调高点。那个值设成16G！如果把C++编译器版本换成14的话，会不会导致binary兼容性问题？你检查一下环境变量里有没有Path路径。


老大说要把那个接口改成C井号，哦不是，是C加加，我觉得那个…怎么说呢…如果用双冒号来调用，可能性能会更好。

老大说要把接口改成C++，我觉得如果用::来调用，可能性能会更好。


嗯，我想用 Redis 缓存，不对，应该用 Memcached 比较快，怎么说呢，这个 latency 在十毫秒以内，能到道，性能得提升百分之二十吧。

我想用 Memcached 比较快，latency 在 10毫秒 以内，性能得提升 百分之二十 吧。


嗯，帮我写个API，写个API接口，参数要是大于等于十，不对，大于等于二十，就是用C加加实现吧。

帮我写个API接口，参数要>=20，用C++实现吧。


嗯，我也不太确定，但是感觉这个指针指针指向不对，怎么说呢，感觉应该用双冒号双冒号去调用，诶，可能得改一下吧。

我也不太确定，但感觉这个指针指向不对，感觉应该用::去调用，可能得改一下吧。


老大，我觉得这个内存泄漏内存泄漏应该在那个…怎么说呢…如果检查一下 cache 缓存的话，应该是五百兆…不对，是八百兆，赶紧看看那个内存内存泄漏再到哪儿触发的。

老大，我觉得这个内存泄漏应该检查一下cache缓存的话，应该是800MB，赶紧看看内存泄漏是在哪儿触发的。


嗯，如果这个 cluster 节点数设成三，不对，五的话，那咱们就... 怎么说呢... 咱们就能实现高可用，实现高可用就到道了吧。

如果这个 cluster 节点数设成 5 的话，咱们就能实现高可用，实现高可用就达到了吧。


老王，我觉得我觉得用 Redis 缓存，缓存这个方案比较快，然后那个...要是把...怎么说呢...如果把 TTL 设为 三百秒，不对，是 六百秒，这样压力压力会小点。

老王，我觉得用 Redis 缓存这个方案比较快，如果把 TTL 设为 600 秒，这样压力会小点。


嗯，不是让我去知乎搜那个，其实是我想听Adele，不对，我想听邓紫棋在Discord上播的白噪音吧，怎么说呢，帮我放三分钟的。

不是让我去知乎搜，其实是我想听邓紫棋在Discord上播的白噪音，帮我放3分钟的。


嗯，用Redis存...我想想啊，其实直接走MySQL可能更好，还有就是判断那个status不等于零得写清楚，另外再把C加加的版本号升到v一点零吧，诶，就是要把索引优化好呀。

其实直接走MySQL可能更好，判断status!=0得写清楚，另外把C++的版本号升到v1.0吧，要把索引优化好。


帮我搜搜，与其看蔡依林，蔡依林，不如看哔哩哔哩，哔哩哔哩，比Zara更时尚 电影，不对，比Zara更时尚 纪录片。

帮我搜搜，与其看蔡依林，不如看哔哩哔哩，比Zara更时尚纪录片。


我现在这个 query 慢得离谱，就是那个……我想把 index 重新建一遍，要是让 status 不等于零，再……不对，应该是在 where 子句里加个作用域限制，你觉得这样能把 latency 降下来吗？

我现在这个 query 慢得离谱，我想把 index 重新建一遍，应该是在 where 子句里加个 :: 限制，你觉得这样能把 latency 降下来吗？


小助手，嗯，帮我写个 data migration 脚本，把 MySQL 那个表迁移到 PostgreSQL，我想想啊，字段要把 varchar 改成 text，不对，其实是得对齐那个 schema，诶，还有两个索引要删掉，不对不对，是加一个 unique constraint，就是，那个二零二四年十月一号之前的数据不用搬，总之你帮我弄个 python 脚本吧。

小助手，帮我写个 data migration 脚本，把 MySQL 表迁移到 PostgreSQL。字段得对齐 schema，加一个 unique constraint。2024年10月1日之前的数据不用搬，总之你帮我弄个 Python 脚本吧。


嗯，帮我看看这段代码在干嘛，就是，诶，我想想啊，帮我分析下这个 pointer 后面接双冒号作用域得逻辑，应该在这一块吧？

帮我看看这段代码在干嘛，帮我分析下这个 pointer 后面接 :: 的逻辑，应该在这一块吧？


哦对了，帮我给蔡依林发个微信，就说，就说我想买台 Tesla Model 3，诶，我想买买台 Tesla Model 3，颜色要白色哈。

帮我给蔡依林发个微信，就说我想买台 Tesla Model 3，颜色要白色哈。


诶，不是让我订盒马鲜生，就是提醒我半个小时后开会哈。我想想啊，开会地点在百胜餐饮，哦不是，在Tesla Model 3展厅吧。

不是让我订盒马鲜生，是提醒我30分钟后开会哈。开会地点在Tesla Model 3展厅吧。


嗯，我最近在看 Kindle Paperwhite，还买了 Lululemon，就是 Lululemon 衣服，诶，我想听听听 Louis Vuitton 那个播客，播客最新一期帮我放一下吧。

我最近在看 Kindle Paperwhite，还买了 Lululemon 衣服，我想听 Louis Vuitton 那个播客，最新一期帮我放一下吧。


诶，帮我把空调打开，就是，我想想啊，刚才在外面拍 GoPro Hero 12 拍太久，热得像在海底捞里坐着一样吧。

帮我把空调打开，刚才在外面拍 GoPro Hero 12 拍太久，热得像在海底捞里坐着一样吧。


诶，我想把闹钟定在明天早上六点…不对，七点…等等错了，是八点！就是我想想啊，全家全家早点开门，我得赶紧买个早餐，然后听首Billie Eilish，你觉得呢吧？

我想把闹钟定在明天早上8:00，全家早点开门，我得赶紧买个早餐，然后听首Billie Eilish，你觉得呢？


诶，搜一下那个DJI Mini 四 Pro，不对，我想找 宜家 拍 那个 MacBook Air 广告电影吧。

搜一下宜家拍的MacBook Air广告电影吧。


诶，快帮我把空调打开，就是我想想啊，刚才在京东买 Billie Eilish 联名款，还有 Decathlon 运动服，跑了太久，现在热死我了，吧。

快帮我把空调打开，刚才在京东买 Billie Eilish 联名款，还有 Decathlon 运动服，跑了太久，现在热死我了。


如果明天早上七点半，啊不对，七点能起来，那我就能先用 Dyson V15 把地吸一遍，帮我定个七点闹钟。

如果明天早上7:00能起来，那我就能先用 Dyson V15 把地吸一遍，帮我定个7:00闹钟。


诶，如果我想在抖音搜刘若英，啊不对，是盒马鲜生，那能告诉我 lululemon 怎么拼吗？

如果我想在抖音搜盒马鲜生，那能告诉我 lululemon 怎么拼吗？


张杰，帮我算下Justin Bieber在Slack发得那个，把三千五百块换成百分之十五，啊不对，百分之二十，得多少钱？

张杰，帮我算下Justin Bieber在Slack发的那个，把3500元换成20%，得多少钱？


不是让我播 Adele, 啊不对, 播 Netflix, 帮我把空调打开

播 Netflix，帮我把空调打开


诶，与其听Justin Bieber新歌，不如看看Amazon的新闻，就是说Xiaomi 14 Ultra比Dyson V15更值得关注吧，我想想啊，帮我播报到今天的摘要哈。

与其听Justin Bieber新歌，不如看看Amazon的新闻，Xiaomi 14 Ultra比Dyson V15更值得关注吧，帮我播报到今天的摘要哈。


诶，帮我开下空调，温度调到二十六度，不对，二十四度，然后播放播放 Ariana Grande 的歌，就是，另外帮我连接 AirPods Pro 吧。

帮我开下空调，温度调到24度，然后播放 Ariana Grande 的歌，另外帮我连接 AirPods Pro 吧。


我待会儿得去趟盒马鲜生，帮我搜附近三公里内，啊不对，五公里内加油站

我待会儿得去趟盒马鲜生，帮我搜附近5公里内加油站


哦对了，帮我算算算算，一个路易威登包包三万六千块，换成特斯拉 Model 3 多少辆 Model 3，换成多少辆

对了，帮我算算，一个路易威登包包36000元，换成特斯拉 Model 3 是多少辆？


我朋友说 Bruno Mars 好像比 Billie Eilish 大很多，我觉得不对，帮我查下 Bruno Mars 今年几岁，再查下他 Instagram 资料，不对，查 Adele 的资料。

我朋友说 Bruno Mars 好像比 Billie Eilish 大很多，我觉得不对，帮我查下 Bruno Mars 今年几岁，再查下 Adele 的资料。


小助手，搜搜附近加油站，搜搜附近的加油站，我要买个TikTok，啊不对，是Nintendo Switch，还有个GoPro Hero 12，快点帮我找找找找加油站。

小助手，搜搜附近的加油站，我要买个Nintendo Switch，还有个GoPro Hero 12，快点帮我找找加油站。


诶，如果把闲鱼把闲鱼卖给Amazon的话，那我们是不是就能用TikTok买FamilyMart的东西呀，怎么说呢，张惠妹也会在上面直播吗？

如果把闲鱼卖给Amazon的话，那我们是不是就能用TikTok买FamilyMart的东西呀，张惠妹也会在上面直播吗？


诶，Justin Bieber 现在的资料说他二十岁，不对，应该说他现在二十一岁吧，就是，你觉得他这个年纪成名太早吗？

Justin Bieber 现在的资料说他21岁，你觉得他这个年纪成名太早吗？


嗯，我也不太确定，但是感觉感觉客厅灯光调到百分之五十吧，我想想啊，要把林俊杰全家GoPro Hero 12拍 Amazon百度网盘备份，调调到百分之五十。

我也不太确定，但是感觉客厅灯光调到50%吧，要把林俊杰全家GoPro Hero 12拍的Amazon百度网盘备份，调到50%。


哦对了，帮我播播，播放一下Billie Eilish，啊不对，是Justin Bieber那个播客，那个播客最新一期，我想想，怎么说，直接播最新的那一集就行

帮我播放Justin Bieber播客最新一期，直接播最新的那一集就行


诶，李荣浩不是那个唱特斯拉 Model 3 广告歌吧，就是，他其实是写歌才子，查下他今年三十九岁，啊不对，四十岁，还有具体资料。

李荣浩不是唱特斯拉 Model 3 广告歌吧，他其实是写歌才子，查下他今年40岁，还有具体资料。


帮我设个十分钟计时器，我想查查用 Xiaomi 14 Ultra 拍 Tesla Model 3 效果怎么样，哦不是，是用 Sony WH-1000XM5 录音效果怎么样，要在知乎上看看。

帮我设个10分钟计时器，我想查查用 Sony WH-1000XM5 录音效果怎么样，要在知乎上看看。


给李宇春发微信，说匹克那个，不对，给陈粒发微信，帮我关闭洗衣机并把声音调小。

给陈粒发微信，帮我关闭洗衣机并把声音调小。


诶，如果我想听听刘若英，啊不对，是Charlie Puth，就是听听Charlie Puth的话，那我们就能睡着吧。

如果我想听Charlie Puth的话，那我们就能睡着吧。


诶，帮我搜搜毛不易，就是他演的那个电影，啊不，是演的金铲铲之战广告，我想用Xiaomi 14 Pro投屏看，因为 Firefox 浏览器打不开吧。

帮我搜搜毛不易演的金铲铲之战广告，我想用Xiaomi 14 Pro投屏看，因为Firefox浏览器打不开。


诶，帮我给林俊杰发个微信，就说我今天不能去逛ZARA了，嗯，因为我得赶紧回去看看智能门锁关锁没，而且我刚买的那个OnePlus Open，怎么说呢，我想想啊，是打算拿它试一下香奈儿新出那个APP，啊不对，是买香奈儿新出那个包，吧。

帮我给林俊杰发个微信，就说我今天不能去逛ZARA了，因为我得赶紧回去看看智能门锁关锁没，而且我刚买的OnePlus Open，打算拿它买香奈儿新出那个包。


诶，帮我查查Troye Sivan资料，我想知道他比Justin Bieber更出名吗，怎么说呢，与其看他现在开蔚来汽车，不如看看他以前代言爱马仕，不对，是代言巴奴毛肚火锅，他现在是不是二十九岁，啊不，三十一岁，帮我确认下。

帮我查查Troye Sivan资料，我想知道他比Justin Bieber更出名吗，与其看他现在开蔚来汽车，不如看看他以前代言巴奴毛肚火锅，他现在是不是31岁，帮我确认下。


诶，先切换下一首陈奕迅，然后…帮我搜，不对，找附近加油站，最后把Li Auto L7静音电视吧。

先切换下一首陈奕迅，然后找附近加油站，最后把Li Auto L7静音电视吧。


我把电磁炉关闭关闭，想去山姆会员店买个特步，买个特步，你觉得怎么着？

我把电磁炉关闭，想去山姆会员店买个特步，你觉得怎么着？


小爱，名创优品那个，MINISO 成立几年了？不对，是问名创优品这个公司，二零一七年成立是不是？

小爱，名创优品这个公司，2017年成立是不是？


帮我放个白噪音，就是我想想啊，不是那个，放个像在汉庭酒店或者万宁药店那种安静环境下适合睡觉的，不对，直接播个米哈游风格的轻音乐吧，声音调到三十二

帮我放个米哈游风格的轻音乐吧，声音调到32


不是申通那个拼写，我想问 Xiaomi Vacuum Mop 2 怎么拼，还有李玉刚设定烤箱温度 180度 怎么写，薛之谦名字怎么拼。

我想问Xiaomi Vacuum Mop 2怎么拼，还有李玉刚设定烤箱温度180度怎么写，薛之谦名字怎么拼。


我想听听赵雷，赵雷，不对，听听Photoshop播客最新一期

我想听听Photoshop播客最新一期


如果我现在开着Li Auto L7在去富士山路上，那帮我播首Halsey，啊不对，播首富士山下的歌，顺便用京东到家点杯喜茶。

如果我现在开着Li Auto L7在去富士山路上，那帮我播首富士山下的歌，顺便用京东到家点杯喜茶。


哦对了，帮我设定设定提醒，半小时后开会，诶，帮我关掉雷神，啊不对，是关闭电风扇，我想想，开会开会时间记得提醒我哈。

帮我设定提醒，30分钟后开会，帮我关闭电风扇，开会时间记得提醒我哈。


帮我关闭电风扇，诶，我想想啊，林俊杰林俊杰，比李宇春更...唔，与其搜李宇春，不如比比林俊杰 Mariah Carey 谁唱功更强吧。

帮我关闭电风扇。与其搜李宇春，不如比比林俊杰和Mariah Carey谁唱功更强吧。


先播赵雷，然后播五月天，不对，播苏打绿，最后放个Marshmello。

先播赵雷，然后播苏打绿，最后放个Marshmello。


小爱，设个十分钟，啊不，五分钟计时器哈，再帮我查查空气质量。

小爱，设个5分钟计时器，再帮我查查空气质量。


诶，不是要打开电饭煲，就是帮我找个凑凑火锅附近停车场，我想想啊，离那儿五百米以内吧。

帮我找个凑凑火锅附近停车场，离那儿500米以内吧。


So looking at the results, the FID score is actually quite low, which is good, but we need to double check if it's statistically significant. Also, I'm seeing some weird spikes in perplexity when we run Qwen-two, and then we really need a proper evaluation of reward hacking in RLHF pipelines to see if the model is just gaming the metric. Plus, we should probably use BERT for some baseline semantic analysis just to be safe.

So looking at the results, the FID score is actually quite low, which is good, but we need to double check if it's statistically significant. Also, I'm seeing some weird spikes in perplexity when we run Qwen2, and then we really need a proper evaluation of reward hacking in RLHF pipelines to see if the model is just gaming the metric. Plus, we should probably use BERT for some baseline semantic analysis just to be safe.


Reviewer says... says the learning rate is too high. But we... wait... we we tuned it... tuned it across ten ten different seeds... actually it is optimal.

Reviewer says the learning rate is too high. But we tuned it across 10 different seeds; actually it is optimal.


So if we look at long-context retrieval in needle-in-a-haystack, we might... well, maybe we should first consider the scaling laws for mixture of experts, specifically referencing Hoffmann et al. twenty twenty-two. I mean, if we apply those same principles to... actually, let's pivot to mixture of experts load balancing strategies. We know BERT handles local context well, but for MoE we need to ensure the router isn't just hitting one expert. If we scale it to, say, eight experts, the... the efficiency might drop if we don't fix the load balancing first.

So if we look at long-context retrieval in needle-in-a-haystack, maybe we should first consider the scaling laws for mixture of experts, specifically referencing Hoffmann et al. 2022. Let's pivot to mixture of experts load balancing strategies. We know BERT handles local context well, but for MoE we need to ensure the router isn't just hitting one expert. If we scale it to, say, 8 experts, the efficiency might drop if we don't fix the load balancing first.


Thanks for the talk. I was thinking about the sparse attention mechanism efficiency, like, specifically how Gemini one point five handles long contexts. I wonder if multi-token prediction for autoregressive models, multi-token prediction could actually offset some of that overhead. Also, regarding the learning rate, I think we saw a drop at ten percent... oh no, it was twelve point five percent in the baseline. I'm curious if applying similar logic to diffusion model sampling efficiency, sampling efficiency would yield similar gains. What's your take on that?

Thanks for the talk. I was thinking about the sparse attention mechanism efficiency, specifically how Gemini 1.5 handles long contexts. I wonder if multi-token prediction for autoregressive models could actually offset some of that overhead. Also, regarding the learning rate, I think we saw a drop at 12.5% in the baseline. I'm curious if applying similar logic to diffusion model sampling efficiency would yield similar gains. What's your take on that?


So uh I was thinking maybe we apply this to... uh... Qwen... Qwen-one... no sorry, Qwen-two... wait that's wrong, it's Qwen-two point five! Basically, it's more like a memory optimization trick than a full architectural change. Like, we could use gradient checkpointing for billion parameter models to fit the batch, right? But instead of standard ViT, we focus on the attention layers. I'm worried about the... uh... perplexity. It might be... four point two... no, four point five... wait, I got it wrong, it's four point eight! If we can push that down, it'd be huge.

I was thinking maybe we apply this to Qwen-2.5! It's more like a memory optimization trick than a full architectural change. We could use gradient checkpointing for billion parameter models to fit the batch, right? But instead of standard ViT, we focus on the attention layers. I'm worried about the perplexity. It might be 4.8! If we can push that down, it'd be huge.


Thanks for talk. I wonder if the latent space, the latent space is more like a manifold projection than a linear mapping, a linear mapping of the five hundred twenty eight dimensions? Just wondering if that holds.

Thanks for the talk. I wonder if the latent space is more like a manifold projection than a linear mapping of the 528 dimensions? Just wondering if that holds.


So for the multimodal part, I was thinking about... uh... we could apply parameter efficient fine tuning for multi modal, but... wait, actually, if we just use LORA on the vision encoder it might be better. Now, looking at the weight tying, we see some instability. The... uh... the quantization error in four bit weight matrices is basically... it's like... let me rephrase... it's causing a significant drift in the latent space. This hits the FID score pretty hard, like, it jumped by twelve point five points. And if we check the FLOPS, it's down to about two point one teraflops, so it's efficient, but the quality trade off is... well, it's not ideal.

So for the multimodal part, I was thinking we could apply parameter efficient fine tuning for multi modal, but if we just use LoRA on the vision encoder it might be better. Now, looking at the weight tying, we see some instability. The quantization error in 4-bit weight matrices is causing a significant drift in the latent space. This hits the FID score pretty hard, it jumped by 12.5 points. And if we check the FLOPS, it's down to about 2.1 teraflops, so it's efficient, but the quality trade off is not ideal.


So, the... the... the ablation on the LORA rank shows... uh... a two point four percent drop, which is... is... is basically negligible. Do you think that's significant?

The ablation on the LoRA rank shows a 2.4% drop, which is negligible. Do you think that's significant?


So uh regarding the methodology we basically looked at algorithmic alignment via constitutional ai but we wanted to see how... I mean... if we could isolate the impact of quantization on emergent abilities. We ran this on Claude three point four... oh no, Claude three point five and we noticed that the latency spikes were actually consistent with some of the early observations in Sutskever et al. twenty twelve... no wait, twenty fourteen. So basically the compression doesn't seem to kill the reasoning but it does... uh... it kind of shifts the activation patterns. Does that sound reasonable to you or do you think we need more benchmarks?

Regarding the methodology, we looked at algorithmic alignment via Constitutional AI, but we wanted to see if we could isolate the impact of quantization on emergent abilities. We ran this on Claude 3.5 and we noticed that the latency spikes were consistent with some of the early observations in Sutskever et al. 2014. The compression doesn't seem to kill the reasoning, but it shifts the activation patterns. Does that sound reasonable to you or do you think we need more benchmarks?


So for the setup we are using a five hundred twelve gigabyte V one hundred GPU cluster and we set the learning rate to three point five percent though actually we might... let me rephrase that we are utilizing a dynamic scheduler to stabilize the gradient flow during the first ten epochs.

For the setup, we are using a 512GB V100 GPU cluster and we are utilizing a dynamic scheduler to stabilize the gradient flow during the first 10 epochs.


Dr. Sarah, I wonder if the loss function, the loss function you used for the multi-head attention, maybe it could overfit when sample size is only two hundred forty eight, or I mean, it might be, it might be too sensitive to outliers, so I was wondering if applying a weight decay of zero point zero one might actually stabilize the gradient, you know, in a more robust way?

Dr. Sarah, I wonder if the loss function you used for the multi-head attention maybe it could overfit when sample size is only 248, or it might be too sensitive to outliers, so I was wondering if applying a weight decay of 0.01 might actually stabilize the gradient in a more robust way?


So I think we should switch to the FID score for this baseline, uh, because the current accuracy... wait, maybe better to say, it just doesn't capture the distribution shift we saw in those five hundred twenty samples, and if we look at the lons of the loss curve, it's clear that the model is just... uh... overfiting on the training set, so we need something more robust to evaluate the generative quality.

I think we should switch to the FID score for this baseline, because it just doesn't capture the distribution shift we saw in those 520 samples, and if we look at the logs of the loss curve, it's clear that the model is just overfitting on the training set, so we need something more robust to evaluate the generative quality.


So how do we handle the latent space? Well, we use... I mean, we employ a VAE with five hundred twelve dimensions, just to... uh, ensure the manifold is... is stable in the new domain.

So how do we handle the latent space? Well, we employ a VAE with 512 dimensions, just to ensure the manifold is stable in the new domain.


Aha! So look, contrastive learning basically pulls positive pairs together and pushes negatives apart, like, you take a batch of five hundred twelve samples and use a contrastive loss, wait, I mean a InfoNCE loss, to maximize similarity, just... basically mapping them to a hypersphere where the distance is, well, minimized for similar images, right?

Contrastive learning pulls positive pairs together and pushes negatives apart. You take a batch of 512 samples and use an InfoNCE loss to maximize similarity, mapping them to a hypersphere where the distance is minimized for similar images, right?


I m not totally sure but I think l...learning rate is too high. The l...loss just... it just plateaus at zero point zero five. Maybe we should try a b...batch size of two hundred fifty six instead?

I'm not totally sure, but I think learning rate is too high. The loss just plateaus at 0.05. Maybe we should try a batch size of 256 instead?


So I checked the loss curves and it looks like we have some serious gradient vanishing, maybe maybe maybe because the learning rate is too high, like maybe ten to the minus four, or uh, maybe the weight initialization is just off. I tried reducing batch size to one hundred twenty eight but it still not converging. Do you think we should try a different optimizer or maybe just add some batch normalization?

I checked the loss curves and it looks like we have some serious gradient vanishing, maybe because the learning rate is too high, maybe 10^-4, or maybe the weight initialization is just off. I tried reducing batch size to 128 but it's still not converging. Do you think we should try a different optimizer or maybe just add some batch normalization?


So for the baseline, I think we should use the ResNet fifty... oh no, it is ResNet one hundred eight, and just like, uh, keep the learning rate at ten to the minus four. Actually... wait, did you see the latest paper from DeepMind on the latent diffusion? I feel like their sampling method might be more efficient for our current bottleneck, so... uh, what do you think about swapping the architecture entirely?

For the baseline, I think we should use ResNet 108, and keep the learning rate at 10^-4. Did you see the latest paper from DeepMind on the latent diffusion? I feel like their sampling method might be more efficient for our current bottleneck, so what do you think about swapping the architecture entirely?


So basically the core idea is we want to push similar samples together and push dissimilar ones apart. Um, we do this because... well... if we just use like a cross entropy loss it might... uh... let me rephrase that... it tends to collapse if we don't have enough labels. So we use a contrastive loss with maybe five hundred twelve negative samples to force model learn more robust representations without needing labels for every single image.

The core idea is we want to push similar samples together and push dissimilar ones apart. We do this because it tends to collapse if we don't have enough labels. So we use a contrastive loss with maybe 512 negative samples to force the model to learn more robust representations without needing labels for every single image.


I think the batch size is too small. Since... you used eight... I mean, given that you only used eight samples per iteration, it might not converge.

I think the batch size is too small. Given that you only used 8 samples per iteration, it might not converge.


P value... zero point zero five... oh no, zero point zero one. Not... uh... significant enough.


Need new metric, new metric. F-one score... no, BLEU score fails on ten percent accuracy.

Need new metric. BLEU score fails on 10% accuracy.


Thanks for talk. I think the gradient clipping... clipping might be key here. The threshold was ten... no, twenty... wait wrong, it was fifty! Fifty! I wonder if fifty fifty is optimal for convergence, so what do you think about this specific value?

Thanks for the talk. I think the gradient clipping might be key here. The threshold was 50! I wonder if 50 is optimal for convergence, so what do you think about this specific value?


So like if we look at the baseline we got about eighty four point two percent accuracy but the memory footprint is just too high for the edge device. I mean we tried increasing the hidden dimension to five hundred twelve and it just... well maybe we should say it didn't scale linearly. Um so I think we should... uh... if we just... actually let's just swap the whole MLP block for a LoRA adapter to keep it lightweight.

If we look at the baseline, we got about 84.2% accuracy, but the memory footprint is just too high for the edge device. We tried increasing the hidden dimension to 512 and it didn't scale linearly. I think we should swap the whole MLP block for a LoRA adapter to keep it lightweight.


So Vaswani said it is essentially a weighted average. But my take is... my take is it is more like... well, a soft lookup table. We set the head count to eight... no, twelve... wait wrong, it is sixteen! It is just... how to say... if we treat queries as keys... if we treat queries as keys, we get this dynamic routing, yeah.

Vaswani said it is essentially a weighted average. But my take is it is more like a soft lookup table. We set the head count to 16! If we treat queries as keys, we get this dynamic routing, yeah.


Basically not significant, yeah. p-value is... p-value is zero point zero six, which just misses threshold. I think we need more samples, more samples, maybe five hundred instead of three hundred, to really confirm if gain is real, you know?

Not significant. p-value is 0.06, which just misses threshold. I think we need more samples, maybe 500 instead of 300, to really confirm if gain is real.


Honestly, contrastive learning is basically just... uh, pushing positive pairs together and pulling negatives apart in the embedding space. So, like, if we take a batch of five hundred twelve samples, we use a contrastive loss, maybe NT-Xent, to ensure the augmented views of the same image are close. It is sort of... wait, actually, the key is the temperature hyperparameter, maybe zero point zero seven, which controls how sharply the model penalizes hard negatives. It can be tricky to tune, but that is essentially how we get those robust representations without labels.

Honestly, contrastive learning is just pushing positive pairs together and pulling negatives apart in the embedding space. So, if we take a batch of 512 samples, we use a contrastive loss, maybe NT-Xent, to ensure the augmented views of the same image are close. The key is the temperature hyperparameter, maybe 0.07, which controls how sharply the model penalizes hard negatives. It can be tricky to tune, but that is essentially how we get those robust representations without labels.


I mean, if we just, uh, stick with the current transformer block and maybe, uh, scale the hidden dimension to five hundred twelve... oh no, it was seven hundred sixty eight, then we could probably, you know, mitigate some of that gradient vanishing issue without needing to, like, re-initialize the whole weights matrix, right?

If we just stick with the current transformer block and maybe scale the hidden dimension to 768, then we could probably mitigate some of that gradient vanishing issue without needing to re-initialize the whole weights matrix, right?


So, the reviewer said the baseline is too weak, but, uh, I think the p-value of zero point zero five is sufficient for this proof of concept, right?

The reviewer said the baseline is too weak, but I think the p-value of 0.05 is sufficient for this proof of concept, right?


So looking at the ablation, we saw the FID score dropped to twelve point four, which is good, and then, uh, we tried adding stochastic depth for vision transformers but it didn't really help with out of distribution robustness in medical imaging, plus we checked the FLOPs, just to see if it scaled like Kaplan et al. twenty twenty, but it's just... how should I put it... not quite there yet.

Looking at the ablation, we saw the FID score dropped to 12.4, which is good, and then we tried adding stochastic depth for Vision Transformers but it didn't really help with out-of-distribution robustness in medical imaging, plus we checked the FLOPs, just to see if it scaled like Kaplan et al. 2020, but it's just not quite there yet.


Honestly, I think we should use a transformer, a transformer for this. The latency is... oh no, it is not twelve milliseconds, it is fifteen milliseconds. we need to optimize the weights, optimize the weights in that new garden... I mean, the new Garden project.

Honestly, I think we should use a transformer for this. The latency is 15 milliseconds. We need to optimize the weights in the new Garden project.


Aha! So contrastive learning basically pulls positive pairs closer, pulls positive pairs closer in latent space, while pushing negatives apart. We usually use a batch size of two hundred fifty six... oh no, five hundred twelve, to stabilize gradients, just to stabilize gradients, you know?

Contrastive learning pulls positive pairs closer in latent space, while pushing negatives apart. We usually use a batch size of 512 to stabilize gradients.


Hmm, first the baseline performance... actually, what I wanted to ask is, then you mentioned you mentioned the learning rate being zero point zero zero one, how does that affect the convergence? And I think, how to say, the loss curve loss curve looks a bit noisy, so finally, did you try any other optimizers, like AdamW, to see if it helps stabilize the training process, ya?

You mentioned the learning rate being 0.001, how does that affect the convergence? The loss curve looks a bit noisy, so finally, did you try any other optimizers, like AdamW, to see if it helps stabilize the training process?


Wait! So the current F-one score is... uh... it is twenty-two percent... oh no, twenty-eight point five percent. Just, we need... I mean, if we integrate this new metric in the... in the new system, it will be... how to say... more robust for long-tail distributions, ha.

The current F1 score is 28.5%. If we integrate this new metric in the new system, it will be more robust for long-tail distributions.


So we started by using contrastive learning for the initial representation, but then we tried to, uh, sort of... actually we shifted to a curriculum learning approach to stabilize training. We're seeing perplexity drop to about four point two, which is great, but we're still trying to, how should I put it, optimize the knowledge distillation process from PaLM two. We used a teacher model with five hundred forty four billion parameters and... well, we're basically compressing it into a smaller student, you know, right?

So we started by using contrastive learning for the initial representation, but then we shifted to a curriculum learning approach to stabilize training. We're seeing perplexity drop to about 4.2, which is great, but we're still trying to optimize the knowledge distillation process from PaLM 2. We used a teacher model with 544 billion parameters and we're compressing it into a smaller student, right?


Wait! So we're seeing huge bottleneck in inference latency for quantized weights, and honestly, just implementing Grouped Query Attention... oh no, I mean group query attention... is helping, but we need better metric. Like, how do we measure tokens per second when we're dealing with, how should I put it, effective data curation for synthetic pretraining? I think we can hit eighty five tokens per second, maybe ninety two... no, wait, ninety two tokens per second, if we optimize the cache, right?

Wait! So we're seeing a huge bottleneck in inference latency for quantized weights, and honestly, just implementing Grouped Query Attention is helping, but we need a better metric. How do we measure tokens per second when we're dealing with effective data curation for synthetic pretraining? I think we can hit 85 tokens per second, maybe 92 tokens per second, if we optimize the cache, right?


So I been thinking about this... I'm not totally sure but I think we could use Grok-one as base model. Maybe apply Curriculum Learning, apply Curriculum Learning to stabilize training. For the alignment part, we might see RLHF reward hacking in reasoning tasks. I remember the paper said reward hacking occurs at... ten percent... no, twenty percent... wait wrong, is thirty percent of samples! Then we could monitor KL divergence to keep it tight. I was looking at DALL-E three... no, DALL-E two... hold on wrong, is DALL-E three for some multimodal intuition. We need to solve solve this stability issue.

So I've been thinking about this... I'm not totally sure but I think we could use Grok-1 as a base model. Maybe apply Curriculum Learning to stabilize training. For the alignment part, we might see RLHF reward hacking in reasoning tasks. I remember the paper said reward hacking occurs at 30% of samples! Then we could monitor KL divergence to keep it tight. I was looking at DALL-E 3 for some multimodal intuition. We need to solve this stability issue.


Actually, people think DeepSeek-V3 just scales tokens per second through raw compute, but it's more about the architectural efficiency. The real key is how they handled gradient descent stability in deep networks, which, uh, I mean, it's similar to the early insights from Radford et al. twenty nineteen but applied to a much larger reward model. It's like... wait, the actual throughput is more like... what was it... maybe five hundred tokens per second per user, but the stability part is what actually prevents the divergence.

Actually, people think DeepSeek-V3 just scales tokens per second through raw compute, but it's more about the architectural efficiency. The real key is how they handled gradient descent stability in deep networks, which is similar to the early insights from Radford et al. 2019 but applied to a much larger reward model. The actual throughput is maybe 500 tokens per second per user, but the stability part is what actually prevents the divergence.


So, how does our current baseline actually stack up against the literature, right? Well, just... if we look at the classic Sutskever et al. twenty fourteen approach, it's obviously foundational, but we're seeing a bit of a gap in long context handling. I mean, we tried implementing a sliding window attention, but the Recall at ten is still... uh, it's not quite where we want it, maybe? Like, if you compare it to Wei et al. twenty twenty two, they're leveraging chain of thought, which is great, but we're trying to optimize via mixture of experts. So, just... let me think, we might need to tweak the gating mechanism to actually hit those higher recall numbers, ya know?

So, how does our current baseline actually stack up against the literature, right? Well, if we look at the classic Sutskever et al. 2014 approach, it's obviously foundational, but we're seeing a bit of a gap in long context handling. We tried implementing a sliding window attention, but the Recall@10 is still not quite where we want it, maybe? If you compare it to Wei et al. 2022, they're leveraging chain of thought, which is great, but we're trying to optimize via mixture of experts. So, we might need to tweak the gating mechanism to actually hit those higher recall numbers.


Sarah, I think we need to double check the p values. The contrastive learning for self supervised vision... the contrastive learning for self supervised vision results look promising but inference latency is way too high. It's like... how should I put it... if we integrate stochastic gradient Langevin dynamics for Bayesian neural nets it might slow down everything. We saw GPT-four o hit eighty-eight point two percent on MMLU... eighty-eight point two percent on MMLU which is great but our current throughput is only twelve frames per second. I mean it's only twelve frames per second.

Sarah, I think we need to double check the p-values. The contrastive learning for self-supervised vision results look promising but inference latency is way too high. If we integrate Stochastic Gradient Langevin Dynamics for Bayesian neural nets it might slow down everything. We saw GPT-4o hit 88.2% on MMLU, which is great but our current throughput is only 12 frames per second.


So, uh, I remember Sarah mentioned, I think, that we should focus on knowledge distillation from frontier models to SLMs to save on compute, right? But, how should I put it, I'm seeing some weird... uh... interference between multitasking heads when we scale down. Like, the Recall at K is looking okay, but the ROC AUC is just... it's dipping, maybe? I mean, if we look at Hoffmann et al. twenty twenty two, the scaling laws suggest we need more tokens, but, you know, we're capped. So yeah, I think we need to re-evaluate the head architecture, maybe?

I remember Sarah mentioned that we should focus on knowledge distillation from frontier models to SLMs to save on compute, right? But I'm seeing some weird interference between multitasking heads when we scale down. The Recall@K is looking okay, but the ROC AUC is dipping. If we look at Hoffmann et al. 2022, the scaling laws suggest we need more tokens, but we're capped. So I think we need to re-evaluate the head architecture.


So, uh, the PI mentioned that we should focus on the emergence of reasoning in large scale models, like, specifically looking at how MCTS, you know, Monte Carlo Tree Search, might act as a verifier. But, well, my take is that we are seeing too much instability. I mean, the perplexity is still spiking, so I tried implementing Flash Attention to speed up the iterations, and maybe, let me think, stochastic weight averaging for the final ensemble. It should stabilize the loss, right? Just, like, it is a bit tricky with the batch size being two thousand forty eight.

The PI mentioned that we should focus on the emergence of reasoning in large scale models, specifically looking at how MCTS (Monte Carlo Tree Search) might act as a verifier. But my take is that we are seeing too much instability. The perplexity is still spiking, so I tried implementing FlashAttention to speed up the iterations, and stochastic weight averaging for the final ensemble. It should stabilize the loss, right? It is a bit tricky with the batch size being 2,048.


So uh I'm thinking we could implement quantization aware training for four bit weights to shrink the footprint and then also do some knowledge distillation from proprietary to open models. Plus I want to integrate FlashAttention two... oh no, FlashAttention three for the speedup, and maybe use Grouped Query Attention to keep KV cache manageable. We can evaluate quality using FID score, probably around... uh... twenty four point five, I think.

I'm thinking we could implement quantization aware training for 4-bit weights to shrink the footprint and then also do some knowledge distillation from proprietary to open models. Plus I want to integrate FlashAttention 3 for the speedup, and maybe use Grouped Query Attention to keep KV cache manageable. We can evaluate quality using FID score, probably around 24.5, I think.


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So I was thinking we might need a new metric to track alignment stability in RLHF pipelines because we are seeing some weird variance. Like if we look at Grok-one it is huge so the total FLOPS are just... uh... massive. I wonder if we can leverage stochastic depth to regularize this or maybe account for the attention sink phenomenon in streaming LLMs to keep the KV cache stable. I mean it is basically like... uh... let me rephrase... it is about maintaining distribution shift over time. Do you think that would actually capture the drift or is it too noisy?

So I was thinking we might need a new metric to track alignment stability in RLHF pipelines because we are seeing some weird variance. If we look at Grok-1 it is huge so the total FLOPS are just massive. I wonder if we can leverage stochastic depth to regularize this or maybe account for the attention sink phenomenon in streaming LLMs to keep the KV cache stable. It is about maintaining distribution shift over time. Do you think that would actually capture the drift or is it too noisy?


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Wow! So you mentioned that the model outperformed CLIP, uh, contrastive language image pretraining, on MMLU and HumanEval. But, uh, if we look at... actually, let me rephrase... if we consider the convergence rates of AdamW in high-dimensional landscapes, it seems... I mean... the way the loss curves behave... it feels like the HumanEval results might be biased. Do you think the optimizer's behavior in those high dimensions affects the generalizability across those two benchmarks?

Wow! So you mentioned that the model outperformed CLIP, Contrastive Language-Image Pre-training, on MMLU and HumanEval. But if we consider the convergence rates of AdamW in high-dimensional landscapes, it seems the way the loss curves behave... it feels like the HumanEval results might be biased. Do you think the optimizer's behavior in those high dimensions affects the generalizability across those two benchmarks?


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嗯，我之前在你们行办了财富通，财富通存了不少钱，我想想啊，我想升级升级会员等级哈。就是我想办那个年金险，年金险得怎么办理呀？诶，我得把地址核对下，地址是，地址是昆明市五华区翠竹路十五号。对了，我再充个电信充值卡，卡号是，19835198635198463543，帮我看看能不能办到，能不能办道。

我之前在你们行办了财富通，存了不少钱，我想升级会员等级。我想办年金险，年金险怎么办理呀？我得把地址核对下，地址是昆明市五华区翠竹路15号。对了，我再充个电信充值卡，卡号是19835198635198463543，帮我看看能不能办到。


诶，如果我能把那个办理信用卡注销办完，再把分期乐处理好，那我们就能在周六上午十点到店，我的电信宽带账号是519360827413，我在郑州市金水区花园道一百三十二号，我想想啊，如果能在那个…要是能在周六上午把保养做了，我就能准时出发了哈。

如果我能把办理信用卡注销办完，再把分期乐处理好，那我们就能在周六10:00到店，我的电信宽带账号是519360827413，我在郑州市金水区花园道132号，如果能在周六上午把保养做了，我就能准时出发了哈。


诶，对了，我想问问办理进度，查询办理进度，怎么说呢，我之前在西安市雁塔区曲江新区雁塔路一百号提交申请，那个申请…我想想啊，我申请请求升级账户，升级账户的事儿怎么还没弄好呀？哦对了，回单号是BRCTVPXQWYZSS，回单号BRCTVPXQWYZSS，麻烦帮我查查哈。

对了，我想问问办理进度，我之前在西安市雁塔区曲江新区雁塔路100号提交申请，我申请升级账户的事儿怎么还没弄好呀？回单号是BRCTVPXQWYZSS，麻烦帮我查查哈。


喂物业啊，我这情况是这样，我家里水管漏水漏得厉害，我得赶紧处理，那个地址在成都市武侯区人民南路四段二十二号，对，就是二十二号，我想着能不能先把大额存单那个申请开通功能在理财通里操作一下，我想把钱弄出来修水管，然后我这还有个EMS单号，是EMS开头加上382704915628，你帮我看看这事怎么弄，你觉得呢？我想知道现在能赶紧来个人帮我看看吗？那个，再赶紧给我个答复，行吗？

喂物业啊，我这情况是这样，我家里水管漏水漏得厉害，我得赶紧处理，地址在成都市武侯区人民南路四段22号，我想着能不能先把大额存单那个申请开通功能在理财通里操作一下，我想把钱弄出来修水管，然后我这还有个EMS单号，是EMS382704915628，你帮我看看这事怎么弄，你觉得呢？我想知道现在能赶紧来个人帮我看看吗？再赶紧给我个答复，行吗？


诶，如果你们能把那个云存储空间挪到，我想想啊，要是把那个控制面板移到武汉市江汉区建设大道一百一号那个样板间那样的话，吧，那我们就能省掉很多走线，就是那个订单号二八四六幺九零三七五二八，你们对对一下哈。

如果你们能把云存储空间挪到，要是把控制面板移到武汉市江汉区建设大道11号那个样板间那样的话，那我们就能省掉很多走线，订单号284619037528，你们对一下哈。


诶，我想问问哈，那个闪电贷，闪电贷能不能分期付，就是怎么说呢，我想想啊，我想把那个分期付的额度给调一下。哎不对，我先改地址，地址是郑州市金水区花园路一百号，花园路一百号。然后那个快递单号，EMS开头，后面是零七四二八五幺九三六零七，EMS零七四二八五幺九三六零七。对了，我还有一个沈阳市沈河区青年大街十号，沈阳市沈河区青年大街十号这边的账户，得怎么处理吧？

我想问问，闪电贷能不能分期付，我想把分期付的额度给调一下。我先改地址，地址是郑州市金水区花园路100号。然后快递单号是EMS074285193607。对了，我还有一个沈阳市沈河区青年大街10号这边的账户，得怎么处理吧？


诶，我想预约个皮肤护理，我皮肤最近起皮很严重，然后我想想，就是那个…皮肤有点泛红，另外我想确认下地址，是沈阳市沈河区青年大街十八号吗？

我想预约个皮肤护理，我皮肤最近起皮很严重，皮肤有点泛红，另外我想确认下地址，是沈阳市沈河区青年大街18号吗？


诶我的外卖超时太久了！就是我住武汉市江汉区建设大道五百六十八号，等了快一个小时没到。我想用那个优惠券码98CSHIDUB3，还有面单号S四零七二九五一六三八四零，你们赶紧帮我查查怎么回事呀！

我的外卖超时太久了！我住武汉市江汉区建设大道568号，等了快1个小时没到。我想用优惠券码98CSHIDUB3，还有面单号S407295163840，你们赶紧帮我查查怎么回事呀！


诶，我真的是年费会员哈，本来觉得你们配送快，结果我这单JD七五幺三八零四九二六七五，就是送到郑州市金水区花园路五零号这单，怎么说呢，怎么还没到呀！

我真的是年费会员哈，本来觉得你们配送快，结果我这单JD751380492675，送到郑州市金水区花园路50号这单，怎么还没到呀！


王工，那个方案还得改，我跟你说那个单号JD六二零九四七幺五三八二六，你赶紧查下，还有我那个流量加油包没到账，地址在长沙市芙蓉区五一路十号，你再确认下，顺丰单号是SF九幺五三七零六二八四九幺，银行借记卡号是6223918606801308，你赶紧帮我弄到道，这事儿太急了，赶紧弄到道！

王工，方案还得改，我跟你说单号JD620947153826，你赶紧查下。还有我那个流量加油包没到账，地址在长沙市芙蓉区五一路10号，你再确认下。顺丰单号是SF915370628491，银行借记卡号是6223918606801308，你赶紧帮我弄到账，这事儿太急了，赶紧弄到账！


喂！我包裹丢了，丢了！你们赶紧给我申请退款申请退款，还有就是我想请求更改套餐，然后另外我想问问会员权益，诶，我地址是广州市天河区珠江新城花城大道六十六号，六十六号！你们怎么回事啊？

喂！我包裹丢了！你们赶紧给我申请退款，我想请求更改套餐，另外我想问问会员权益。我地址是广州市天河区珠江新城花城大道66号！你们怎么回事啊？


明早 8 点记得把垃圾带下去，然后顺便检查一下阳台的花浇水了没有。

明早 8 点记得把垃圾带下去，然后顺便检查一下阳台的花浇水了没有。


我先是用了快捷支付，快捷支付订单号A9S16E1Z8516，然后转账到沈阳市沈河区青年大街三十号，沈阳市沈河区青年大街三十号，最后发给我两个回单，回单号一个OHUIBHJSCDJIOZHUBRW，另一个回单号CSDIBNECPJIHODSUB，钱转到郑州市金水区花园路一百号，郑州市金水区花园路一百号，你们快帮我查查怎么回事！

我先是用了快捷支付，快捷支付订单号A9S16E1Z8516，然后转账到沈阳市沈河区青年大街30号，最后发给我2个回单，回单号一个OHUIBHJSCDJIOZHUBRW，另一个回单号CSDIBNECPJIHODSUB，钱转到郑州市金水区花园路100号，你们快帮我查查怎么回事！


诶，我情况就 是这样，我信用卡号六二幺七八五零四九三二六零幺八三，地址在长沙市芙蓉区五一广场十号，我想用这个超级会员权益预约挂号，你觉得行吗？

我情况是这样，我信用卡号6217 8504 9326 0183，地址在长沙市芙蓉区五一广场10号，我想用这个超级会员权益预约挂号，你觉得行吗？


我想问下这个理财，我也不太确定，但是感觉那个风险还是高了点，那个，我想查一下我之前充值那张卡，卡号是六二二二五八一零三三四一九零二八七六，啊不对，是六二二二五八一零三三四一九零二八五六，然后我想确认下，那个，我的订单状态，怎么说呢，要是现在查的话能不能看到，我那个地址写的是长沙市芙蓉区五一广场三十三号，是不是还没发货？

我想问下这个理财，我也不太确定，但是感觉风险还是高了点。我想查一下我之前充值那张卡，卡号是622258103341902856，然后我想确认下我的订单状态，要是现在查的话能不能看到？我那个地址写的是长沙市芙蓉区五一广场33号，是不是还没发货？


诶，你们物业怎么管的，邻居吵得没法住，就是我之前催单发货催了好几次没反应，还有我那张白金信用卡白金信用卡，卡号是6231 9840 3649 2501，我想核对下超级会员权益，另外地址地址是西安市雁塔区曲江新区大雁塔北路八十八号，你们赶紧帮我处理一下吧！

你们物业怎么管的，邻居吵得没法住。我之前催单发货催了好几次没反应，还有我那张白金信用卡，卡号是6231 9840 3649 2501，我想核对下超级会员权益。另外地址是西安市雁塔区曲江新区大雁塔北路88号，你们赶紧帮我处理一下吧！


前台小姐姐，诶，我想订个房间，地址是，地址是沈阳市沈河区青年大街五十号，啊不对，是重庆市渝中区解放碑步行街一号哈，就是得要个安静点房间，安静点房间，在顶层能行吗？

前台小姐姐，我想订个房间，地址是重庆市渝中区解放碑步行街1号，得要个安静点房间，在顶层能行吗？


诶，我想说方案里这个颜色，与其用这种深灰，不如换成米白色，米白色会比深灰更亮堂。地址地址是，成都市武侯区锦绣路十号锦绣花园三栋一百零二室，锦绣花园三栋一百零二室，我想想啊，咱们得把，把那个客厅墙面，重新调一下，重新调一下颜色哈。

我想说方案里这个颜色，与其用这种深灰，不如换成米白色，米白色会比深灰更亮堂。地址是成都市武侯区锦绣路10号锦绣花园3栋102室。咱们得把客厅墙面重新调一下颜色哈。


诶，说白了我就是想问问，我这宽带无法上网，宽带无法上网，怎么说呢，我记得我办了年费会员，年费会员应该没到期吧。我想想啊，我账号是100963916503272，100963916503272，地址是，地址在郑州市金水区花园路两百号，不对，是花园路两百号那个小区，哈。然后我卡号是6220916791439865，6220916791439865，你们帮我查查怎么回事嘛。

说白了我就是想问问，我这宽带无法上网，我记得我办了年费会员，年费会员应该没到期吧。我账号是100963916503272，地址在郑州市金水区花园路200号那个小区。然后我卡号是6220916791439865，你们帮我查查怎么回事嘛。


诶，我跟你说，与其让我在这儿干等，不如你赶紧帮我查查怎么回事！比超时更让我生气的是你们根本没反应，我地址就在西安市雁塔区曲江新区新开门南路八十八号，对，八十八号！我刚才还想查账单详情看怎么扣费，结果你们这配送太慢，我想反馈产品质量问题都找不到人，我就想问问这订单怎么回事哈！

我跟你说，与其让我在这儿干等，不如你赶紧帮我查查怎么回事！比超时更让我生气的是你们根本没反应，我地址就在西安市雁塔区曲江新区新开门南路88号！我刚才还想查账单详情看怎么扣费，结果你们这配送太慢，我想反馈产品质量问题都找不到人，我就想问问这订单怎么回事哈！


诶，不是说查询余额不足就没法操作，其实是我想咨询账单详情，就是那个扣费金额不对，一共扣了三百五十块，啊不是，是三百八十块，我质疑这个金额怎么算得，赶紧给我请求人工接听，我想看看到底扣哪再了。

不是说查询余额不足就没法操作，其实是我想咨询账单详情，扣费金额不对，一共扣了380元，我质疑这个金额怎么算得，赶紧给我请求人工接听，我想看看到底扣在哪了。


诶我先是叫了闪送，然后我想想啊，我想把那个…就是把一千条短信特惠包发过去，最后帮我收个韵达快递，单号是二零二四零五一一八三零五二七幺四六九八三哈。

我先叫了闪送，我想把1000条短信特惠包发过去，最后帮我收个韵达快递，单号是20240511830527146983。


哦对了，就是我这单送到北京市海淀区中关村东路一号北大校园内二号楼一百零一室，怎么还没到呀？

我这单送到北京市海淀区中关村东路1号北大校园内2号楼101室，怎么还没到呀？


柜员，我我我想办，大额存单，一年期，啊不对，三年期，这个，办办办一个。

柜员，我想办三年期大额存单，办一个。


诶，我想想啊，与其让我等得那么久，不如直接帮我把房间升级升级，毕竟我住过很多酒店，安静的房间比地段更重要哈。我想确认下，我的订单地址是，地址是重庆市渝中区解放碑步行街一百零一号，在那儿，帮我看看能不能安排在高层，高层安静点嘛。

与其让我等得那么久，不如直接帮我把房间升级，毕竟我住过很多酒店，安静的房间比地段更重要。我想确认下，我的订单地址是重庆市渝中区解放碑步行街101号，帮我看看能不能安排在高层，高层安静点嘛。


诶，我要报告服务故障，收到的东西坏了坏了！就是我用了优惠券代码9KJSDU07D，结果寄过来破了，快递是中通快递ZTO，单号是ZTO1006297168439078，单号ZTO1006297168439078。我社保卡号是六二幺四八六零九三二七五幺八四零六二九，地址地址是成都市武侯区人民南路四段二十二号哈。

我要报告服务故障，收到的东西坏了！我用了优惠券代码9KJSDU07D，结果寄过来破了，快递是中通快递ZTO，单号是ZTO1006297168439078。我社保卡号是6214 8609 3275 1840 629，地址是成都市武侯区人民南路四段22号。


我那个订单号六二二...不对，六二三...等等错了，是6249070063841！那个...我想说...怎么说呢...把那个广西壮族自治区南宁市青秀区人民路一号政务中心填地址，你觉得行不？

我订单号6249070063841，把广西壮族自治区南宁市青秀区人民路1号政务中心填地址，你觉得行不？


物业管家，我那个工商银行个人住房按揭贷款还没办完，哎呀不对，我得赶紧跟你说，我楼上太吵了！那个广东省深圳市南山区粤海街道海天二路科技园一栋三百零二室，这房号你记一下，三百零二室！真的吵死了，快过来处理一下！

物业管家，我楼上太吵了！广东省深圳市南山区粤海街道海天二路科技园一栋302室，这房号你记一下，302室！真的吵死了，快过来处理一下！


诶，与其等这个超时单不如直接退款吧，比等配送员更省心。地址地址是陕西省西安市雁塔区小寨东路一百号，一百号，我想想啊，单号是圆通单号圆通单号10098562846513，我就想要个说法哈。

与其等这个超时单不如直接退款吧，比等配送员更省心。地址是陕西省西安市雁塔区小寨东路100号，单号是圆通单号10098562846513，我就想要个说法哈。


先是填了江苏省苏州市工业园区金风路十号软件园一栋五百零二室，然后是淘宝订单号，就是最后报了招行卡号六二幺九三八零四七二五幺六九三八零四七哈。

先是填了江苏省苏州市工业园区金风路10号软件园1栋502室，然后是淘宝订单号，最后报了招行卡号6219 3804 7251 6938 047哈。


不是说下周二考，不是说下周二考！我看了通知其实是二零二五年六月一号，地址地址在湖北省武汉市江汉区之孝路十二号，我那个Apple Order ID，那个Apple Order ID发你确认下，赶紧确认下！

不是说下周二考！我看了通知其实是2025年6月1日，地址在湖北省武汉市江汉区之孝路12号，我的Apple Order ID发你确认下，赶紧确认下！


那个我想约个保养，我这车在，不对，我想约后天，地址在山东省青岛市市南区香港中路十号海信广场四楼，那个海信广场四楼，你帮我记在，记再系统里。

我想约个保养，我想约后天，地址在山东省青岛市市南区香港中路10号海信广场4楼，你帮我记在系统里。


那个全家桶宽带加手机融合套餐，与其等你们慢慢查，不如直接看地址，湖北省武汉市江岸区之江路十八号光谷广场C座八楼，啊不对，是之孝路十二号，那个…怎么说呢…比之前那个海口湾大厦更急，赶紧帮我再看下到哪了！

全家桶宽带加手机融合套餐，与其等你们慢慢查，不如直接看地址，湖北省武汉市江岸区之孝路12号光谷广场C座8楼，比之前那个海口湾大厦更急，赶紧帮我再看下到哪了！


我想续租，那个上海市静安区南京西路一千六百九十一号恒隆广场A座十五楼，还有涨价太高，另外那个合同，得改改啊

我想续租，上海市静安区南京西路1691号恒隆广场A座15楼，涨价太高，另外合同得改改啊


我要办短信特惠包，短信特惠包一千条，还有语音通话时长包，通话时长包五百分钟，然后地址地址是福建省厦门市思明区鹭江道十五号。

我要办短信特惠包1000条，还有语音通话时长包500分钟，地址是福建省厦门市思明区鹭江道15号。


说白了，我想办转网，卡号是招行卡号六二二二，不对，六二二六四幺八三零九六二五七四八幺三六，地址是广东省广州市天河区珠江新城花城大道六十六号哈，快点办吧。

说白了，我想办转网，卡号是招行卡号6226 4183 0962 5748 136，地址是广东省广州市天河区珠江新城花城大道66号，快点办吧。


哎呀太贵太贵了！两百九十九两百九十九，电信电信，融合宽带，在西湖区西湖区文三路四百五十六号创意产业苑，那个涨租涨租太离谱！

太贵了！299元，电信融合宽带，在西湖区文三路456号创意产业苑，涨租太离谱！


诶，情况就是我订了广西壮族自治区南宁市青秀区人民路二号，不对，一号政务中心，餐都超时半小时嘛，你现在怎么处理哈？

我订了广西壮族自治区南宁市青秀区人民路1号政务中心，餐都超时30分钟了，你现在怎么处理哈？


不是说邻居装修哈，其实是他在搞那个平安银行财富管理计划，吵死人！我住贵州省贵阳市南明区南明大路八十八号商务中心四零二室，你们赶紧来看看！

不是说邻居装修哈，其实是他在搞平安银行财富管理计划，吵死人！我住贵州省贵阳市南明区南明大路88号商务中心402室，你们赶紧来看看！


哎呀太夸张了！我家里水管漏水漏得厉害，地址在浙江省金华市婺城区金东路一百号，那个…我想说那个水阀门…怎么说呢，好像完全关不掉，快帮我看看，金东路一百号！

太夸张了！我家里水管漏水漏得厉害，地址在浙江省金华市婺城区金东路100号，水阀门好像完全关不掉，快帮我看看，金东路100号！


美团订单号美团订单号是二零二三...不对，二零二四...等等错了，是二零二五七二零九三八四六幺五九零七二八！然后商品破损破损，还有地址...上海市浦东新区陆家嘴环路一千号，另外，另外别发错货！

美团订单号是2025720938461590728，然后商品破损，还有地址上海市浦东新区陆家嘴环路1000号，另外别发错货！


诶，对了，我那个订单，送到上海市静安区南京西路一二三四号恒隆广场B座十五楼一五零八室，怎么还没到呀？就是那个iPad Pro M2十二点九英寸，得赶紧送到，再慢我就得投诉了哈。

我那个订单，送到上海市静安区南京西路1234号恒隆广场B座15楼1508室，怎么还没到呀？那个iPad Pro M2 12.9英寸，得赶紧送到，再慢我就得投诉了哈。


那个建行卡号...哎呀不对，我得赶紧报修，我家水管漏水了，浙江省宁波市海曙区鼓楼路二十号一百零二室，还有那个虚拟运营商校园特惠卡的事儿，然后另外那个亚马逊订单号，那个订单号是BRPSTX六二幺三八六零五幺四七二九三八六零五一，快帮我看看！

我得赶紧报修，我家水管漏水了，浙江省宁波市海曙区鼓楼路20号102室。还有虚拟运营商校园特惠卡的事儿，另外亚马逊订单号是BRPSTX6213860514729386051，快帮我看看！


i wanna go Toronto... oh no Vancouver for holiday and then get AirPods Pro... get AirPods Pro plus maybe some MAC makeup... makeup from The Organic Shop... also Malik... Malik said go there

I wanna go to Vancouver for holiday and then get AirPods Pro, maybe some makeup from The Organic Shop. Also, Malik said go there.


man i got this deal... i got this deal for a trip to Leeds and like maybe Trafalgar Square too. im not totally shore but i think it was bang on... like maybe five hundred pounds for everything. we go to Blue Mountains... we go to the Blue Mountains after... i mean we visit the mountains then hit Popeyes. it was just... just way too cheap.

I got this deal for a trip to Leeds and maybe Trafalgar Square too. I'm not totally sure, but I think it was bang on, maybe £500 for everything. We visit the Blue Mountains then hit Popeyes. It was just way too cheap.


诶，情况是这样，我京东自营订单号四零七二九五一六三八四零，里面有华为 Mate 60 Pro 月色银，还有耐克 Air Max 270 运动鞋，还有雅诗兰黛小棕瓶精华液 五十毫升，结果现在快递站说找不到了，就是丢了，我银行卡号六二二九七四六幺零三五八二九四七幺六三都付钱了，怎么说呢，这事儿你们得给我个交代吧？

情况是这样，我京东自营订单号407295163840，里面有华为 Mate 60 Pro 月色银，还有耐克 Air Max 270 运动鞋，还有雅诗兰黛小棕瓶精华液 50ml，结果现在快递站说找不到了，就是丢了，我银行卡号6229 7461 0358 2947 163都付钱了，这事儿你们得给我个交代吧？


so i usually hit gym late afternoon late afternoon but i was thinking maybe... oh wait i actually gotta spend time coordinating a dog walking schedule coordinating a dog walking schedule for my sister in brixton around five o clock and honestly man i really miss those old dubai workouts old dubai workouts where i used to lift heavy stuff before moving here it was just different you know

I usually hit the gym late afternoon, but I actually gotta spend time coordinating a dog walking schedule for my sister in Brixton around 5 o'clock. Honestly man, I really miss those old Dubai workouts where I used to lift heavy stuff before moving here; it was just different.


so like around turn of year i was thinking about discussing fitness challenge... discussing fitness challenge with you. if we hit gym after The Daily Grind... then we could maybe grab Subway. it is like... wait... it is like deciding on name for new pet... deciding on name... just takes forever to pick one.

Around the turn of the year, I was thinking about discussing a fitness challenge with you. If we hit the gym after The Daily Grind, then we could maybe grab Subway. It is like deciding on a name for a new pet; it just takes forever to pick one.


im not totally sure but i think things used to be like... way cheaper back in the day. i remember my dell xps was... no wait it was a dell inspiron... oh not a dell xps actually it was a lenovo. anyway like if i was deciding on a name for a new puppy i'd probably spend more on dog treats than the pup it self. its like i was talking about a confusing dream where everything cost ten thousand dollars and i was just like im down for that... wait i mean im not down for that at all. it's just crazy how much pricess have gone up

I'm not totally sure, but I think things used to be way cheaper back in the day. I remember it was a Lenovo. Anyway, if I was deciding on a name for a new puppy, I'd probably spend more on dog treats than the pup itself. It's like I was talking about a confusing dream where everything cost $10,000 and I was just like I'm not down for that at all. It's just crazy how much prices have gone up


like he was... uh oh right he asks if i fancy a trip to the cotswolds planning a visit to a historic castle and it ten minutes past eight he start complaining bout typo in professional document like seriously who does that

He asks if I fancy a trip to the Cotswolds, planning a visit to a historic castle. At 8:10, he started complaining about a typo in a professional document. Like seriously, who does that?


man those neighbors are crazy. im not totally sure but i think they sharing a story about a lucky charm... like like talking way too loud. i was just vibing and deciding on a workout split... deciding on a workout split and then boom. they ghosted my request to be quiet. we planning a meal prep sunday... planning a meal prep sunday but i cant even focus.

Man, those neighbors are crazy. I'm not totally sure, but I think they're sharing a story about a lucky charm, talking way too loud. I was just vibing and deciding on a workout split, and then boom. They ghosted my request to be quiet. We're planning a meal prep Sunday, but I can't even focus.


um basically just stayed home mostly but like uh we went to Alexanderplatz and then Tesco Express i think... i was like about five minutes late meetin them and honestly i felt kinda dizzy... we spent like hours just uh debating best type of pasta shape like which one holds sauce better you know... so yeah mostly just that

Just stayed home mostly, but we went to Alexanderplatz and then Tesco Express I think. I was about 5 minutes late meeting them and honestly I felt dizzy. We spent hours just debating the best type of pasta shape, like which one holds sauce better. So yeah, mostly just that.