Ok, enough jokes for a while. I’ll go back to reading bedtime stories for now.
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Alvin Sng
@alvinsng
Factory
FactoryMTS at @FactoryAI; previously at: Meta, Airbnb, Brex & Opendoor
6.1KFollowers
624Following
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"Gone are the days when you could coast on one hit. Do that, and you'll be acquired by Bending Spoons at a fraction of your value."
This mindset keeps us winning.
This mindset keeps us winning.
Eno Reyes@EnoReyes · Oct 7Factory is a product company. In a year when a lot of smart money is betting on neoservices that is starting to sound contrarian.
The AI wave makes it tempting to sell outcomes and deliver them with people. We think the bigger opportunity is software that gets better every time it's used. That's what we build for our customers, and it's how we run Factory.
The products we build should help our customers build their own capacity to ship better software, faster. We think of the product as everything from the first time you hear about Factory through your Nth renewal.
Building the right product starts with the old advice: listen to your customers' problems, not their solutions. In 2023, many people believed we should have built autocomplete. We understood that we instead had to build toward self-improving software and software factories, since that is what would come next.
That is our product goal: self-improving software, which continuously improves from the data feedback loop of its users. Self-improving software doesn't happen on its own. You have to be data-backed and optimize the signal → deploy loop. That requires a software factory operating continuously and evolving alongside the software it produces.
The factory also has to know what good looks like. It has to help the organization extract its tacit knowledge and embed it into the software that operates the business. Taste is not sufficient. You must enforce the unique decisions that define your product. And it doesn't stop at the product. The entire company is going to be defined by software that listens to the rhythm of the business and enforces it. The clear goal of a leader is to ensure the quality and correctness of these systems.
We run Factory this way. Our unique product and technical decisions are codified as customizations to our harness, automations, CI/CD, lint rules, etc. Factory agents sit in our shared Slack channels, where they build context and execute tactical work on demand.
It also changes how we organize. Your Engineering, Product, and Design team will ship your org chart, so you must design an organization where incentives are maximally aligned. Our pod structure maps directly to our product taxonomy, which gives each pod deep expertise and agency. We work carefully to make sure the product feels cohesive and not like multiple disconnected teams. This is not easy, and we still haven't perfected it. We keep the minimal number of layers between leaders and the organizational lines. 1:25 is not unreasonable for a true people manager.
Our product operating system is itself self-improving software. It aggregates feedback, tracks launches across the team, and organizes our work. With it, our Head of Product @TaylorTheSavage runs a product surface that would otherwise require an army of PMs with just a handful of high-ownership, high-leverage ones.
When an org is designed this way, its impact scales with software, not headcount. That is the real difference between a product company and a services company. Selling outcomes is right. Delivering them with people is services. Neoservices arbitrage the gap between how fast the technology moves and how fast buyers adopt it, and some will become large businesses. But that gap closes every time a product company sells the same buyer a better long-term solution. The market already knows how to value services: Infosys does about $20B in revenue and trades at roughly 2x, not 50x, and it's down about 40% this year.
Being a product company is harder. @danlovesproofs once told me something I very much agree with. Product companies are like movie studios. You have to keep producing blockbusters, and that helps you build talent, brand, and resources. Gone are the days when you could coast on one hit. Do that, and you'll be acquired by Bending Spoons at a fraction of your value. The war is always being waged, and no winner is ever truly ahead by much. And demand for the movies is up.
That's true even against the model labs. There is a value gap between the models and outcomes for users. Anyone can close that gap, and there's no reason to believe any company has an automatic right to win the products that matter most. If anything, access to the full pareto frontier of cost and quality gives model-independent, product-focused companies a focus, cost, and quality advantage.
If someone asked me what to build today, I'd say go build a product company. It is harder, but the biggest outcomes are only possible through product.
Open quoted post →The AI wave makes it tempting to sell outcomes and deliver them with people. We think the bigger opportunity is software that gets better every time it's used. That's what we build for our customers, and it's how we run Factory.
The products we build should help our customers build their own capacity to ship better software, faster. We think of the product as everything from the first time you hear about Factory through your Nth renewal.
Building the right product starts with the old advice: listen to your customers' problems, not their solutions. In 2023, many people believed we should have built autocomplete. We understood that we instead had to build toward self-improving software and software factories, since that is what would come next.
That is our product goal: self-improving software, which continuously improves from the data feedback loop of its users. Self-improving software doesn't happen on its own. You have to be data-backed and optimize the signal → deploy loop. That requires a software factory operating continuously and evolving alongside the software it produces.
The factory also has to know what good looks like. It has to help the organization extract its tacit knowledge and embed it into the software that operates the business. Taste is not sufficient. You must enforce the unique decisions that define your product. And it doesn't stop at the product. The entire company is going to be defined by software that listens to the rhythm of the business and enforces it. The clear goal of a leader is to ensure the quality and correctness of these systems.
We run Factory this way. Our unique product and technical decisions are codified as customizations to our harness, automations, CI/CD, lint rules, etc. Factory agents sit in our shared Slack channels, where they build context and execute tactical work on demand.
It also changes how we organize. Your Engineering, Product, and Design team will ship your org chart, so you must design an organization where incentives are maximally aligned. Our pod structure maps directly to our product taxonomy, which gives each pod deep expertise and agency. We work carefully to make sure the product feels cohesive and not like multiple disconnected teams. This is not easy, and we still haven't perfected it. We keep the minimal number of layers between leaders and the organizational lines. 1:25 is not unreasonable for a true people manager.
Our product operating system is itself self-improving software. It aggregates feedback, tracks launches across the team, and organizes our work. With it, our Head of Product @TaylorTheSavage runs a product surface that would otherwise require an army of PMs with just a handful of high-ownership, high-leverage ones.
When an org is designed this way, its impact scales with software, not headcount. That is the real difference between a product company and a services company. Selling outcomes is right. Delivering them with people is services. Neoservices arbitrage the gap between how fast the technology moves and how fast buyers adopt it, and some will become large businesses. But that gap closes every time a product company sells the same buyer a better long-term solution. The market already knows how to value services: Infosys does about $20B in revenue and trades at roughly 2x, not 50x, and it's down about 40% this year.
Being a product company is harder. @danlovesproofs once told me something I very much agree with. Product companies are like movie studios. You have to keep producing blockbusters, and that helps you build talent, brand, and resources. Gone are the days when you could coast on one hit. Do that, and you'll be acquired by Bending Spoons at a fraction of your value. The war is always being waged, and no winner is ever truly ahead by much. And demand for the movies is up.
That's true even against the model labs. There is a value gap between the models and outcomes for users. Anyone can close that gap, and there's no reason to believe any company has an automatic right to win the products that matter most. If anything, access to the full pareto frontier of cost and quality gives model-independent, product-focused companies a focus, cost, and quality advantage.
If someone asked me what to build today, I'd say go build a product company. It is harder, but the biggest outcomes are only possible through product.
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After spawning so many agents, it was about time to spawn a human.
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When I was at Brex, there were moments when things got difficult. But we didn’t just jump ship to Ramp.
Loyalty matters. Your reputation matters. When you choose to join a team, you should own both the wins and the losses.
You often see the pretty side of startups from the outside, but there’s an ugly side to building too. Tough to see this.
Loyalty matters. Your reputation matters. When you choose to join a team, you should own both the wins and the losses.
You often see the pretty side of startups from the outside, but there’s an ugly side to building too. Tough to see this.
Matan Grinberg@matanSF · Sep 30We are terminating Chris Degnan for unethical conduct involving Cognition.
The last few months have seen incredible progress in AI capabilities. San Francisco has flourished as new companies that solve new, more ambitious problems are finding great success. Generally, it is a wonderful time to be building.
We at @FactoryAI have seen overwhelming interest in our model-agnostic coding agents and our software factory product. Our team has 10x’d in size, while our revenue has 100x’d year over year. This momentum has been unprecedented.
A much larger competitor, Cognition (makers of Devin) has fallen behind us on the capabilities that matter most to customers: cost, quality, and security. Instead of competing in the market, Cognition engineers feigned interviews with us to pry information about our product. Not finding what they were looking for, Cognition has decided to throw their weight and money at people with direct knowledge of our most confidential plans.
It feels as though ethics is being thrown out of the window in the AI era. People are willing to do anything, including exploiting privileged information and violating ethical boundaries. I think this is unique to our time, and I don’t think it’s right. Integrity still matters.
Yesterday, I made the decision to immediately terminate Christopher Degnan’s roles as a Board Observer and Advisor to Factory, after over a year of service.
Prior to this decision, Chris told me he had a casual conversation with an executive at Cognition AI. When I questioned his intentions, he reassured me that ethics aside, he had “made too much money” and was “too lazy to go work for Cognition,” which I trusted and believed.
On Monday, Chris spent time advising the Factory team on a handful of confidential board-level matters. That evening, he called me to say that the conversation that was initially described as casual and one-off was actually formal and recurring. For weeks, while he sat in our board meetings and advised our leadership team, he was also confiding with executives of our largest competitor. Chris was subject to confidentiality obligations in connection with his work with Factory. We do not know the extent of the information he shared, but it puts his timely questions about our product roadmap and what the parity gap involves into a new light.
It is sad to sever a relationship with someone who has been a trusted advisor - and even a close friend - for over a year. But Chris’s conduct is unacceptable to me. Trust in Board Membership is one of the sacred bonds in the Silicon Valley, one that helps the startup ecosystem thrive. With it comes an enormous responsibility. That trust was violated.
Competition is good. I respect and in many cases admire our competitors. San Francisco is a beautiful, singular place where the bold and ambitious go to defy the norms and precedents of the past. But certain principles that must remain. Violating ethics to seek advantage turns what should be positive-sum into zero-sum.
We all love technology. And we all love to compete. But we must hold ourselves to a higher standard. The future of software engineering comes with abundance that will impact every person on Earth. Building that future comes with immense responsibility. We must build and compete with integrity.
Open quoted post →The last few months have seen incredible progress in AI capabilities. San Francisco has flourished as new companies that solve new, more ambitious problems are finding great success. Generally, it is a wonderful time to be building.
We at @FactoryAI have seen overwhelming interest in our model-agnostic coding agents and our software factory product. Our team has 10x’d in size, while our revenue has 100x’d year over year. This momentum has been unprecedented.
A much larger competitor, Cognition (makers of Devin) has fallen behind us on the capabilities that matter most to customers: cost, quality, and security. Instead of competing in the market, Cognition engineers feigned interviews with us to pry information about our product. Not finding what they were looking for, Cognition has decided to throw their weight and money at people with direct knowledge of our most confidential plans.
It feels as though ethics is being thrown out of the window in the AI era. People are willing to do anything, including exploiting privileged information and violating ethical boundaries. I think this is unique to our time, and I don’t think it’s right. Integrity still matters.
Yesterday, I made the decision to immediately terminate Christopher Degnan’s roles as a Board Observer and Advisor to Factory, after over a year of service.
Prior to this decision, Chris told me he had a casual conversation with an executive at Cognition AI. When I questioned his intentions, he reassured me that ethics aside, he had “made too much money” and was “too lazy to go work for Cognition,” which I trusted and believed.
On Monday, Chris spent time advising the Factory team on a handful of confidential board-level matters. That evening, he called me to say that the conversation that was initially described as casual and one-off was actually formal and recurring. For weeks, while he sat in our board meetings and advised our leadership team, he was also confiding with executives of our largest competitor. Chris was subject to confidentiality obligations in connection with his work with Factory. We do not know the extent of the information he shared, but it puts his timely questions about our product roadmap and what the parity gap involves into a new light.
It is sad to sever a relationship with someone who has been a trusted advisor - and even a close friend - for over a year. But Chris’s conduct is unacceptable to me. Trust in Board Membership is one of the sacred bonds in the Silicon Valley, one that helps the startup ecosystem thrive. With it comes an enormous responsibility. That trust was violated.
Competition is good. I respect and in many cases admire our competitors. San Francisco is a beautiful, singular place where the bold and ambitious go to defy the norms and precedents of the past. But certain principles that must remain. Violating ethics to seek advantage turns what should be positive-sum into zero-sum.
We all love technology. And we all love to compete. But we must hold ourselves to a higher standard. The future of software engineering comes with abundance that will impact every person on Earth. Building that future comes with immense responsibility. We must build and compete with integrity.
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LA is fashionably late.
SF is fashionably forgetful.
> Forget to reply in Slack until the second “bump.”
> Forget to update your weekly KPIs.
> Forget to create the ticket, so the task never gets done.
Keep it up, and people will presume you’re a high performer with too much on your plate.
SF is fashionably forgetful.
> Forget to reply in Slack until the second “bump.”
> Forget to update your weekly KPIs.
> Forget to create the ticket, so the task never gets done.
Keep it up, and people will presume you’re a high performer with too much on your plate.
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This is why we recently rolled out the Product Vision interview at Factory and require it for all engineering roles.
It’s no longer enough to know how to build something. Engineers increasingly need to know what to build, why it matters, and have the autonomy to take it from idea to impact.
That’s a big part of why Factory stays lean, with <40 engineers while shipping so much. When the same person owns both product and engineering, they have the full context to make better decisions while avoiding the context and coordination taxes that come from working across teams.
See a recent example in action: x.com/alvinsng/statu…
It’s no longer enough to know how to build something. Engineers increasingly need to know what to build, why it matters, and have the autonomy to take it from idea to impact.
That’s a big part of why Factory stays lean, with <40 engineers while shipping so much. When the same person owns both product and engineering, they have the full context to make better decisions while avoiding the context and coordination taxes that come from working across teams.
See a recent example in action: x.com/alvinsng/statu…
Xiaoyin Qu@quxiaoyin · Sep 27Prediction: from now on, great product leaders will get $100M comp package, not researchers.
1. 2022-2026 is all about killer models. Right now models still matter, but only for frontier tasks like science, math, trading etc. etc. You either kill cancer or you don't matter.
2. Instead, great products like muse, grokbot make AI truly accessible for people. The proliferation of AI matters way more now.
3. Open weight models make it cheaper and better, and the margin for most models will go down significantly, leading to less profits.
4. Training techniques are copyable, but unique data is not. All things equal(compute, training techniques), unique data is the new oil. Even if your training techniques are second-tier, with unique first-tier data you win.
5. Great products attract sticky users who provide unique data that lead to even better models.
Therefore, product is the one that matters. We are finally entering the gold era of product visionaries.
Open quoted post →1. 2022-2026 is all about killer models. Right now models still matter, but only for frontier tasks like science, math, trading etc. etc. You either kill cancer or you don't matter.
2. Instead, great products like muse, grokbot make AI truly accessible for people. The proliferation of AI matters way more now.
3. Open weight models make it cheaper and better, and the margin for most models will go down significantly, leading to less profits.
4. Training techniques are copyable, but unique data is not. All things equal(compute, training techniques), unique data is the new oil. Even if your training techniques are second-tier, with unique first-tier data you win.
5. Great products attract sticky users who provide unique data that lead to even better models.
Therefore, product is the one that matters. We are finally entering the gold era of product visionaries.
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We're the real deal
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This may be the last chance to build wealth.
Income via labor is a shrinking share of the economy while capital takes more. Search "BLU Labor share" and you'll find countless articles.
When you buy stock in a self-driving car company, you're buying a robot that earns income for you while you sleep. This is why owning capital matters.
As technology gets better, more economic output will come from machines and AI rather than human labor.
But technology also gives anyone unprecedented leverage: free knowledge, AI to multiply output, cheap distribution, and efficient access to capital. More leverage than any prior era. The opportunity is to use that leverage to build and own assets that produce income, rather than relying solely on selling your time.
Income via labor is a shrinking share of the economy while capital takes more. Search "BLU Labor share" and you'll find countless articles.
When you buy stock in a self-driving car company, you're buying a robot that earns income for you while you sleep. This is why owning capital matters.
As technology gets better, more economic output will come from machines and AI rather than human labor.
But technology also gives anyone unprecedented leverage: free knowledge, AI to multiply output, cheap distribution, and efficient access to capital. More leverage than any prior era. The opportunity is to use that leverage to build and own assets that produce income, rather than relying solely on selling your time.
Craig Weiss@craigweiss · Sep 26this is the best time in human history to build wealth
Open quoted post → 8 17 3 202 37.7K 141
I’ve seen this firsthand:
Vibe coding fails when you can’t tell the difference between a "best practice" on paper and real-world experience. Real experience means factoring in all the trade-offs, from speed and cost to safety, and making the pragmatic choice, which even the best models can’t do. Here are some examples:
1. Data migrations. The model proposed a fleet of parallel cloud functions and new insert endpoints, adding endpoint, invocation, logging, and telemetry costs.
The database was the bottleneck anyway, so a single remote server would have been enough. An agent could run the migration script there, detect unexpected data formats, update the script, and continue migrating as it encountered edge cases. It would have shipped and run twice as fast at half the cost.
2. Database constraints. Models love putting every business rule and enum constraint in the database.
That sounds robust, but it can make shipping painfully slow. Every new enum value or business rule change becomes another database migration. Application-level validation is usually enough. Otherwise, monitor for inconsistencies and alert.
3. Missing variables. Models often add fallback values when an environment variable is missing.
It keeps the app running, which feels convenient, especially during local development. But if infrastructure forgets to set a required variable or uses the wrong name, the app can silently run in development mode. The safer behavior is to fail loudly at startup so the deployment will halt.
Knowing how to code matters because you need to recognize when the model’s "best practice" is wrong and override it.
Vibe coding fails when you can’t tell the difference between a "best practice" on paper and real-world experience. Real experience means factoring in all the trade-offs, from speed and cost to safety, and making the pragmatic choice, which even the best models can’t do. Here are some examples:
1. Data migrations. The model proposed a fleet of parallel cloud functions and new insert endpoints, adding endpoint, invocation, logging, and telemetry costs.
The database was the bottleneck anyway, so a single remote server would have been enough. An agent could run the migration script there, detect unexpected data formats, update the script, and continue migrating as it encountered edge cases. It would have shipped and run twice as fast at half the cost.
2. Database constraints. Models love putting every business rule and enum constraint in the database.
That sounds robust, but it can make shipping painfully slow. Every new enum value or business rule change becomes another database migration. Application-level validation is usually enough. Otherwise, monitor for inconsistencies and alert.
3. Missing variables. Models often add fallback values when an environment variable is missing.
It keeps the app running, which feels convenient, especially during local development. But if infrastructure forgets to set a required variable or uses the wrong name, the app can silently run in development mode. The safer behavior is to fail loudly at startup so the deployment will halt.
Knowing how to code matters because you need to recognize when the model’s "best practice" is wrong and override it.
Hannah@anah_sahh · Sep 25Hot take:
Vibe coding only works well if you already know how to code.
Open quoted post →Vibe coding only works well if you already know how to code.
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Got the life-changing notification on X. Now I’m:
- Sending my two weeks’ notice
- Drafting my new book, Making It
- Hitting up lenders for a mortgage
- Shopping for private jets
Busy day. More to come.
- Sending my two weeks’ notice
- Drafting my new book, Making It
- Hitting up lenders for a mortgage
- Shopping for private jets
Busy day. More to come.
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Same reason airlines don’t like you using Expedia to compare flights.
Save every penny on every prompt, and it all adds up at scale.
Save every penny on every prompt, and it all adds up at scale.
Matan Grinberg@matanSF · Sep 24Big Token hates this one simple trick!
Dynamic model routing allows for a more efficient free market across the model providers.
This puts pricing pressure on them to provide the best/fastest/cheapest tokens for any given task, lest the tokens get routed to a competing model.
Open quoted post →Dynamic model routing allows for a more efficient free market across the model providers.
This puts pricing pressure on them to provide the best/fastest/cheapest tokens for any given task, lest the tokens get routed to a competing model.
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The model providers optimize for raw benchmark scores or they optimize for price per token. Neither captures what customers actually care about: the cost to efficiently complete the work that actually exists.
That’s why we built Legacy: a benchmark representative of the real distribution of software work, and why we evaluate models on the cost to complete those tasks, not just their raw scores.
That’s why we built Legacy: a benchmark representative of the real distribution of software work, and why we evaluate models on the cost to complete those tasks, not just their raw scores.
Factory@FactoryAI · Sep 24Today, our benchmark, Legacy-Bench, joins @FireworksAI_HQ’s Specialized Intelligence Index.
Frontier models have blind spots for legacy code, what’s benchmarked is improved. Legacy-Bench tests how well AI models can debug, extend, and migrate historic systems, to drive the future of software.
Frontier models have blind spots for legacy code, what’s benchmarked is improved. Legacy-Bench tests how well AI models can debug, extend, and migrate historic systems, to drive the future of software.
Open quoted post →
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The most sought-after engineers are the ones with the largest context windows.
They understand the code, product, customers, business, and history, and more importantly, how it all fits together. That context allows them to make better decisions.
These people tend to be endlessly curious. They want to understand how everything works, and when they’re passionate about something, they go deep. Their craft, a hobby, a side project, a random rabbit hole. They don’t just dabble, they keep learning until they really understand it.
They understand the code, product, customers, business, and history, and more importantly, how it all fits together. That context allows them to make better decisions.
These people tend to be endlessly curious. They want to understand how everything works, and when they’re passionate about something, they go deep. Their craft, a hobby, a side project, a random rabbit hole. They don’t just dabble, they keep learning until they really understand it.
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The best commute is no driving.
Technology continues to democratize what the ultra-wealthy always had: a private chauffeur. Now everyone can reclaim headspace to unlock so much more.
Technology continues to democratize what the ultra-wealthy always had: a private chauffeur. Now everyone can reclaim headspace to unlock so much more.
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Sometimes we have a little too much fun to the point where @EnoReyes tells me it's my last day.
Somehow I've been told it's my last day multiple times, yet I'm still here.
Somehow I've been told it's my last day multiple times, yet I'm still here.
Matan Grinberg@matanSF · Sep 23One of the reasons we will win is because we are having more fun
Open quoted post → 1 0 0 27 3.8K 1
Controlled chaos in the Software Factory
Walker Hildebrand@walk_hild · Sep 23Nah I clicked the button, I should get the credit @alvinsng
Open quoted post →
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Half a million views, the people of X concur
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My tweet didn’t even survive a year before tech advanced again. Here’s the post–September 2026 version:
Before Jev: You needed an LLM that was 400× more expensive and 200× slower.
Now: Use an LLM for a one-time setup. Jev executes every event.
Now let’s see how long it takes for this one to become outdated...
Before Jev: You needed an LLM that was 400× more expensive and 200× slower.
Now: Use an LLM for a one-time setup. Jev executes every event.
Now let’s see how long it takes for this one to become outdated...
Alvin Sng@alvinsng · May 22One of my favorite superpowers of agents is building classifiers. It’s insanely high leverage.
Before AI, you needed a year-round team:
- 3 ML engineers to build the models
- 3 ML infra engineers to scale them up
- 2 software engineers to integrate the parts
- 1 data scientist to analyze it
- 1 PM to manage the product
- 0.5 EM to hold it together
Now, in minutes, you can have an agent generate a markdown file that classifies inputs, then let agents run continuously against it.
Below is a Sentry error classifier I generated at @FactoryAI. But you can build this for almost anything: customer-reported bugs, backend traffic analysis, fraudulent payment activity.
Personal use cases too: categorizing credit card transactions, labeling emails, or organizing documents.
Before AI, you needed a year-round team:
- 3 ML engineers to build the models
- 3 ML infra engineers to scale them up
- 2 software engineers to integrate the parts
- 1 data scientist to analyze it
- 1 PM to manage the product
- 0.5 EM to hold it together
Now, in minutes, you can have an agent generate a markdown file that classifies inputs, then let agents run continuously against it.
Below is a Sentry error classifier I generated at @FactoryAI. But you can build this for almost anything: customer-reported bugs, backend traffic analysis, fraudulent payment activity.
Personal use cases too: categorizing credit card transactions, labeling emails, or organizing documents.
Open quoted post →
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10k-line PR: LGTM in 15 sec
10-line PR:
- Line-by-line analysis
- A "quick" huddle on the rationale
- 3 comments left 4 hours later
10-line PR:
- Line-by-line analysis
- A "quick" huddle on the rationale
- 3 comments left 4 hours later
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A MTS at Factory is on a whole new level
Ann Bordetsky@annbordetsky · Sep 20Alvin low key crushing it in my LinkedIn feed this morning @alvinsng
Open quoted post →
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