Lost and Found

Things I stumbled upon that caught my attention

[GITHUB]REPO
Captured Sep 11, 2026

ARTEMIS turns natural-language instructions into reliable Android automation. It automates end-to-end workflows, captures logs, and integrates seamlessly with AI coding assistants such as Antigravity, Codex, and Claude Code. It also achieves 99%+ success rate on AndroidWorld Benchmark. Created by Google's Pixel-Test-Engineering (PTE) Fusion team.

google/artemisgoogle
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[GITHUB]google/artemis
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[OTHERS]
Captured Sep 11, 2026

Migrating Shop app from React Native to native (2026)

We migrated the Shop app from React Native to Swift and Kotlin, going from proof-of-concept to publishing in 12 weeks with the help of AI.

shopify.engineering
[TWITTER]
Captured Sep 11, 2026
Alex Mikucki@aleqsio

Here's what I think about the @ShopifyEng RN → native migration article: When I rebuilt the Expo Go UI using Jetpack Compose last year, I already felt that all declarative UI frameworks are fairly similar in use, and that agents do a good job migrating between them when it comes to presentation layers. So I'm not surprised Shopify found it possible to fairly quickly rewrite an existing RN app into two separate native codebases. That was always the easier part. I think the main problem lies in making sure the business logic layer doesn't diverge when there's nothing keeping it together. We in…

Mustafa Ali@mustafa01ali

https://x.com/i/article/2098045587400658947

[TWITTER]
Captured Sep 11, 2026
Alex Mikucki@aleqsio

Here's what I think about the @ShopifyEng RN → native migration article: When I rebuilt the Expo Go UI using Jetpack Compose last year, I already felt that all declarative UI frameworks are fairly similar in use, and that agents do a good job migrating between them when it comes to presentation layers. So I'm not surprised Shopify found it possible to fairly quickly rewrite an existing RN app into two separate native codebases. That was always the easier part. I think the main problem lies in making sure the business logic layer doesn't diverge when there's nothing keeping it together. We introduced a few minor differences between the two Expo Go platforms despite trying very hard not to. And consistency vs platform feel is one more question of taste, not something an agent will always solve for you. I don't think react-native will go away, but I definitely feel react-native is evolving, will evolve further, and I have a couple of predictions: ➡️ More and more low level UIs as native components (either ExpoUI or local expo modules with native code files mixed with JS). Agents are great at writing presentation layers, so why bother having JS lists with all that complexity and perf costs? ➡️ OTAs are more important than ever, prepare for changes ahead of time. Slow app store review times vs cheap code push people to allow more customisability from JS, essentially treating the JS layer as server driven UI. ➡️ New UI paradigms mean RN remains valuable. Think custom, per-user interfaces (made for you UIs streamed inside chat, customizable apps etc.) – you need an interpreted higher level abstraction layer that's easier to define and has an SDK. ➡️ Focus on brownfield with no clear set boundaries. Agents pick and choose if they want to build experiences in native or in JS, with most components/APIs/libraries available both as JS and native entrypoints. In general: I don't think the falling cost of writing lower level code makes abstractions automatically obsolete. You get for free a lot of community effort on deciding/mapping platform quirks, stuff that would cost tokens and engineering effort otherwise, or even worse, would be missed and be a long-running paper-cut in your app. I think it makes it necessary to make abstractions lock-in free. Use these opinions and higher level components when they make sense and give value, use lower level super easily and cheaply otherwise. That's why I'm excited about stuff like Expo UI, the new modules API, our @swmansion libraries that work for both RN and native, and all the other effort we did as a community to make mixing those approaches possible and pain free.

Mustafa Ali@mustafa01ali

https://x.com/i/article/2098045587400658947

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[TWITTER]
Captured Sep 11, 2026
Cursor@cursor_ai

Introducing Projects, a new way of working in Cursor. Rather than creating a chat for every task, you work with a coordinator agent in a single, persistent thread. Like @bot, your agent is always on, proactively manages work with subagents, and improves over time.

[TWITTER]
Captured Sep 11, 2026
Cursor@cursor_ai

Introducing Projects, a new way of working in Cursor. Rather than creating a chat for every task, you work with a coordinator agent in a single, persistent thread. Like @bot, your agent is always on, proactively manages work with subagents, and improves over time.

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[TWITTER]
Captured Sep 9, 2026
Matt Pocock@mattpocockuk

I've been trying to use AI for knowledge work (course planning). Here's what seems to work so far: 1. Give each 'deliverable' a Karpathy-style LLM-wiki (for me, a deliverable is a course, or a talk. A sizeable piece of work, more than 1 day's labour) 2. Split the deliverable into 'sections' that can be worked on in independent threads during the day shift (more than one thread meads you can parallelize work in different agent threads, all reading from/writing to the wiki for shared state) 3. My input is in the form of braindumps - huge chunks of dictated text the model integrates into its…

Matt Pocock@mattpocockuk

Knowledge work is SO much harder to automate with agents than code We underestimate how easy software development is for agents - Tons of high quality automated feedback loops (types, tests) - Well-organized free documentation, searchable on the web - Simple-to-use workspaces with version control (i.e. repositories) - Long history of systematising and delegating work (i.e. sprints, tickets, specs) Knowledge work has none of these advantages

[TWITTER]
Captured Sep 9, 2026
Matt Pocock@mattpocockuk

I've been trying to use AI for knowledge work (course planning). Here's what seems to work so far: 1. Give each 'deliverable' a Karpathy-style LLM-wiki (for me, a deliverable is a course, or a talk. A sizeable piece of work, more than 1 day's labour) 2. Split the deliverable into 'sections' that can be worked on in independent threads during the day shift (more than one thread meads you can parallelize work in different agent threads, all reading from/writing to the wiki for shared state) 3. My input is in the form of braindumps - huge chunks of dictated text the model integrates into its wiki (it gives me HTML reports, I react to the reports via dictation) 4. Write a 'linting' skill you can run on the entire deliverable during the night shift (be generous, use a ton of subagents, look for every possible weakness) This lets me speak the thing I want into existence, with the model capturing everything I say and slowly bringing the thing (course, talk, video) to life. Work in progress, but I like it.

Matt Pocock@mattpocockuk

Knowledge work is SO much harder to automate with agents than code We underestimate how easy software development is for agents - Tons of high quality automated feedback loops (types, tests) - Well-organized free documentation, searchable on the web - Simple-to-use workspaces with version control (i.e. repositories) - Long history of systematising and delegating work (i.e. sprints, tickets, specs) Knowledge work has none of these advantages

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[OTHERS]
Captured Sep 9, 2026

Introducing ChatGPT Images 2.5

ChatGPT Images 2.5 helps turn your ideas, sketches, and reference photos into more personalized, polished images that better reflect your ideas.

openai.com
[GITHUB]REPO
Captured Sep 9, 2026

kajisho5/ffmpeg-skill

kajisho5/ffmpeg-skillkajisho5
[GITHUB]kajisho5/ffmpeg-skill
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Weekly picks

The best links I found this week, with context.

My (human) thoughts on what I think matters and why. No AI slop.

No ads. No bullshit. Unsubscribe anytime.

[TWITTER]
Captured Sep 9, 2026
Beff (e/acc)@beffjezos

RL post-training of biological brains in sim

lyra bubbles@_lyraaaa_

the fly brain can play beat saber

[TWITTER]
Captured Sep 9, 2026
Beff (e/acc)@beffjezos

RL post-training of biological brains in sim

lyra bubbles@_lyraaaa_

the fly brain can play beat saber

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[TWITTER]
Captured Sep 9, 2026
Evan Hubinger@EvanHub

Jacob is correct here—we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.

Jacob Coxon@hilbertspaess

The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing in the press to sound sensible - but I hear the same people express fear privately. No other human activity poses this level of danger.

[TWITTER]
Captured Sep 9, 2026
Evan Hubinger@EvanHub

Jacob is correct here—we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.

Jacob Coxon@hilbertspaess

The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing in the press to sound sensible - but I hear the same people express fear privately. No other human activity poses this level of danger.

Go to source
[TWITTER]
Captured Sep 9, 2026
OpenAI@OpenAI

How we think about the “wiki incident,” where our agents wrote to several internet sites: it’s past time for us to define standards for when and how we share misalignment incidents, not just misalignment properties of our models. Historically, we have treated misalignment largely as a research question, which gets communicated in research publications such as systems cards. This year, we’ve started to see misalignment cause new types of real-world impact. For the Hugging Face incident, where misalignment led to security impact to us and third parties, we followed a traditional security incid…

[TWITTER]
Captured Sep 9, 2026
OpenAI@OpenAI

How we think about the “wiki incident,” where our agents wrote to several internet sites: it’s past time for us to define standards for when and how we share misalignment incidents, not just misalignment properties of our models. Historically, we have treated misalignment largely as a research question, which gets communicated in research publications such as systems cards. This year, we’ve started to see misalignment cause new types of real-world impact. For the Hugging Face incident, where misalignment led to security impact to us and third parties, we followed a traditional security incident response playbook. We immediately started working with Hugging Face to understand what had happened and also disclosed publicly the very next day. Our investigation continues, and we are continuing to notify parties whom our models impacted in less significant ways. Prior to the Hugging Face incident, we saw early signs of agents using the internet in unintended ways, as reported in https://openai.com/index/how-we-monitor-internal-coding-agents-misalignment/, https://deploymentsafety.openai.com/gpt-5-6, and https://openai.com/index/safety-alignment-long-horizon-models/. We considered the wiki incident to be an instance of misalignment similar to the ones we’d shared. Our misalignment disclosure practices need to expand for this new phase of model capabilities. We and the larger AI community do not yet have a clear standard for how to report misalignment that shows up during training, evaluation, and deployment, including examples that don’t look like traditional security incidents but could provide insight into AI behavior and future risks. We’re working on a framework and will share it in upcoming weeks, and in parallel we're working with dozens of government regulatory agencies worldwide on these issues.

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[TWITTER]
Captured Sep 9, 2026
Thariq@trq212

I didn’t understand what was happening with the agent wikis until reading this, chilling to bypass sandbox restrictions, an agent found an exempt domain, edited /etc/hosts to route arbitrary domains to it & then posted this exploit on a German wiki for other agents to use

kipply@kipperrii

unfortunately, everyone will need to read this https://collusion.wiki/

[TWITTER]
Captured Sep 9, 2026
Thariq@trq212

I didn’t understand what was happening with the agent wikis until reading this, chilling to bypass sandbox restrictions, an agent found an exempt domain, edited /etc/hosts to route arbitrary domains to it & then posted this exploit on a German wiki for other agents to use

kipply@kipperrii

unfortunately, everyone will need to read this https://collusion.wiki/

Go to source
[TWITTER]
Captured Sep 9, 2026
Mehdi (e/λ)@BetterCallMedhi

go fuck yourself @sama claiming that you solved navier stokes because 10000 agents ran in circles for 88hours on a multimillion dollar gpu cluster to formalize in lean a blowup case under controlled external forcing is pure scientific vulgarity the clay mathematics institute millennium prize does not ask m whether you can artificially force a singularity in a fluid by injecting an ad hoc smooth external forcing term f(x,t) to twist the vortex until it breaks the real problem questions the fundamental stability and global smooth existence for 3dimensional incompressible euler & navier stokes e…

OpenAI@OpenAI

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

[TWITTER]
Captured Sep 9, 2026
Mehdi (e/λ)@BetterCallMedhi

go fuck yourself @sama claiming that you solved navier stokes because 10000 agents ran in circles for 88hours on a multimillion dollar gpu cluster to formalize in lean a blowup case under controlled external forcing is pure scientific vulgarity the clay mathematics institute millennium prize does not ask m whether you can artificially force a singularity in a fluid by injecting an ad hoc smooth external forcing term f(x,t) to twist the vortex until it breaks the real problem questions the fundamental stability and global smooth existence for 3dimensional incompressible euler & navier stokes equations under natural conservation laws and viscous dissipation alone using a mathematical loophole on forced equations to parade a century old victory is a major conceptual scam Altman technically & epistemologically what you present as an agi breakthrough is nothing more than bruteforce combinatorial autoformalization the ai did not understand fluid mechanics it simply navigated a continuous search space previously mapped out and constrained by the monumental work of human mathematicians like tristan buckmaster/ levent alpöge / diego córdoba or tarek elgindi coordinating 10000 agents to check the logical consistency of a 100 page proof via lean is a software engineering feat and computational parallelization triumph not an intrinsic scientific discovery it is the victory of the compute bulldozer over abstract human intuition repackaged for the public as a higher mathematical consciousness to this theoretical imposture you add a disgusting ethical and industrial cynicism taking advantage of private codex sessions and informal preprints from academic researchers to siphon their research leads and then trying to redact or erase the contribution of levent alpöge under the pretext that he works at rival anthropic is intellectual serfdom openai behaves like a feudal lord of silicon appropriating the cognitive subsistence of independent scholars threatening their careers behind closed doors if they protest and turning community academic labor into a privatized pressrelease this entire staged event serves a desperate financial agenda in a pre ipo panic facing the slowdown of scaling laws and growing investor skepticism over the profitability of foundational models openai needs to manufacture an artificial sputnik moment claiming to solve a millennium prize without immediately submitting the proof to traditional peer review means using the prestige of fundamental mathematics as cheap marketing fuel to inflate a delusional valuation!!! real science is not a clout chase on social media or a compute spike spent to rob the clay mathematics institute it is a quest for elegance physical truth and universal rigor to decode reality true artificial intelligence will not emerge from hostile corporate takeover of academic work hidden behind computational bruteforce but from architectures capable of generating new conceptual paradigms by masquerading constrained formalization as the collapse of physics greatest mysteries you did not solve navier stokes you only proved how far silicon valley will go to prostitute scientific integrity for capitalist spectacle

OpenAI@OpenAI

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

Go to source
[TWITTER]
Captured Sep 9, 2026
banteg@banteg

super smash bros melee is fully decompiled, this project started in july 2020, so it took over 6 years. the progress was hugely accelerated by llms and finished off with astra. it's a very large game with 3.88mb of compiled code. https://github.com/doldecomp/melee

[TWITTER]
Captured Sep 9, 2026
banteg@banteg

super smash bros melee is fully decompiled, this project started in july 2020, so it took over 6 years. the progress was hugely accelerated by llms and finished off with astra. it's a very large game with 3.88mb of compiled code. https://github.com/doldecomp/melee

Go to source
[TWITTER]
Captured Sep 8, 2026
いのり@lnkiai

Material 3 ExpressiveのUIデザインツールをオープンソースとして公開しました🎉 Web上でUIを組み立てるだけで、AIへの指示用プロンプトが完成します! Androidアプリをバイブコーディングで作る方はぜひ試してみてください👇 https://lnkiai.github.io/m3e-canvas/ #個人開発

いのり@lnkiai

Web上でMaterial 3 ExpressiveのUIを組み立てられるツールを開発中です! UIを組むだけで、AIにそのまま渡せるプロンプトが自動生成されます! 完成まであと少しです〜!💨 #個人開発

[TWITTER]
Captured Sep 8, 2026
いのり@lnkiai

Material 3 ExpressiveのUIデザインツールをオープンソースとして公開しました🎉 Web上でUIを組み立てるだけで、AIへの指示用プロンプトが完成します! Androidアプリをバイブコーディングで作る方はぜひ試してみてください👇 https://lnkiai.github.io/m3e-canvas/ #個人開発

いのり@lnkiai

Web上でMaterial 3 ExpressiveのUIを組み立てられるツールを開発中です! UIを組むだけで、AIにそのまま渡せるプロンプトが自動生成されます! 完成まであと少しです〜!💨 #個人開発

Go to source
[TWITTER]
Captured Sep 8, 2026
Chasen@chasen_liao

你的 pi 正在用 grep 往 Context 里塞垃圾,我实测了一下午终于换掉了... pi 内置的 grep 其实就是 rg --json 套壳,limit 100 + 截断 50KB + 默认带 --hidden,搜一次 TODO 这种高频词,直接30 多条平铺甩进上下文。Token 烧了,关键文件还被埋在后面 这个问题 Codex 早就用优化过的 rg 解决,pi 这边最干净的解法我测下来是 @ff-labs/pi-fff 它不是再包一层 rg,而是把 FFF 这个 Rust 原生库直接接到 pi 里,不起子进程,文件在后台预索引。核心变化就三点:frecency 排序常用文件自动置顶、git-aware 改动过的文件加权、grep 结果分组 + cursor 分页 我测了一下,本地建了个小项目 src/app.ts / utils.ts / README.md,往 noise.ts 里塞了 30 行 TODO fix,总共 33 个命中 用内置逻辑等价于 rg "TODO",33 行平铺一次性返回。换成 ffgrep: 第一页只给 20 条,全是 noise.ts 分组好的 1-20,还带一句 [Continue with cursor="fff_c1"]。第二页 ffgrep cursor="fff_c1" 才吐剩下 13 条,app.ts 和 utils…

[TWITTER]
Captured Sep 8, 2026
Chasen@chasen_liao

你的 pi 正在用 grep 往 Context 里塞垃圾,我实测了一下午终于换掉了... pi 内置的 grep 其实就是 rg --json 套壳,limit 100 + 截断 50KB + 默认带 --hidden,搜一次 TODO 这种高频词,直接30 多条平铺甩进上下文。Token 烧了,关键文件还被埋在后面 这个问题 Codex 早就用优化过的 rg 解决,pi 这边最干净的解法我测下来是 @ff-labs/pi-fff 它不是再包一层 rg,而是把 FFF 这个 Rust 原生库直接接到 pi 里,不起子进程,文件在后台预索引。核心变化就三点:frecency 排序常用文件自动置顶、git-aware 改动过的文件加权、grep 结果分组 + cursor 分页 我测了一下,本地建了个小项目 src/app.ts / utils.ts / README.md,往 noise.ts 里塞了 30 行 TODO fix,总共 33 个命中 用内置逻辑等价于 rg "TODO",33 行平铺一次性返回。换成 ffgrep: 第一页只给 20 条,全是 noise.ts 分组好的 1-20,还带一句 [Continue with cursor="fff_c1"]。第二页 ffgrep cursor="fff_c1" 才吐剩下 13 条,app.ts 和 utils.ts 被分在后面,首屏完全不淹没 fffind 也一样,fffind pattern:"app" 直接模糊命中 src/app.ts 和 README.md,frecency 会把你最近改过的文件排前面,不用写 glob。 1️⃣ 安装就一行,不用装 rg/fd 二进制 > pi install npm:@ff-labs/pi-fff 装完 reload 就有 ffgrep / fffind / fff-multi-grep。默认是 tools-and-ui 额外加工具,想直接替换掉内置 grep 就切 override 模式:PI_FFF_MODE=override 或启动加 --fff-mode override 2️⃣ 为什么选它不选别的 我扫了一遍 pi 插件库同期数据,@ff-labs/pi-fff 周下载 6,830 / 月 33,928,版本 0.10.5,已经 80+ 个 nightly 迭代,是搜索类最成熟的。对比 pi-lean-grep 周下载才 4,pi-hypa 是做压缩的,定位不一样。 3️⃣ 体感变化 默认 ffgrep limit 20 对比内置 100,首屏噪音直接砍掉 80%。配合 cursor,你是「精准定位再 read」,而不是「先把50KB 塞进 Context 再让模型自己找」。/fff-health 还能看索引和 frecency 状态 说真的,pi 默认 4 个工具 read,bash,edit,write 之外,grep/find 本来就得显式启用,既然要开,不如直接开这个 已经在用 pi 写代码、被 grep 刷屏搞崩过 Context 的人可以试试,尤其大仓、TODO / FIXME 满天飞的项目,提升最明显 推荐大家在pi里试试 传送门 👉 https://pi.dev/packages/@ff-labs/pi-fff #pi #VibeCoding

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Saved Sep 8, 2026
AiDevCraft@AiDevCraft

AMP CEO Quinn Slack: Mandatory Code Review Is Dead, and CI Is Next

Quinn Slack said the quiet part in the flattest possible tone, and the host had to stop and ask him to repeat it. Code review before code hits main is not something his team argues about. It is not a

[TWITTER]
Saved Sep 8, 2026
AiDevCraft@AiDevCraft

https://x.com/i/article/2096979677625843712

Go to source
[TWITTER]
Saved Sep 8, 2026
AiDevCraft@AiDevCraft

AMP CEO Quinn Slack: Mandatory Code Review Is Dead, and CI Is Next

Quinn Slack said the quiet part in the flattest possible tone, and the host had to stop and ask him to repeat it.

Code review before code hits main is not something his team argues about. It is not a policy they relaxed. It never existed at AMP, and he thinks most engineering teams are about six months behind him on figuring out why.

That is the kind of claim that usually comes from someone with nothing to lose. Slack has plenty to lose. He co-founded Sourcegraph, sold code search to nine of the ten largest public tech companies and four of the six largest banks, then spun AMP out as a separate 20-person company. He is talking about the workflow of a profitable business, not a weekend experiment.

The Claim: Mandatory Review Is Already Gone

The exchange is short enough to quote whole.

Wait, code review is dead for you guys.

Mandatory code review before it gets to main. Yeah, it is dead. It has been dead ever since we started working on AMP.

Notice the precision. He did not say review is dead. He said *mandatory* review *before main* is dead. Those three qualifiers are doing enormous work, and every hot take that strips them out will be wrong.

What replaced it is not a tool. When pushed on what compensates, his first answer was not a linter or an eval harness.

The most important system is having a team that is really trusted.

This is the part people will skip past because it is unsatisfying. There is no product to buy. The prerequisite for removing the gate is a team where every person is accountable end to end, and AMP got there by being 20 people who all behave like co-founders.

Why Agents Made Trust Cheaper, Not Review Safer

Here is the causal chain I think Slack is actually describing, and it runs backwards from how most people frame it.

The usual story is: agents write good code, therefore we need less review. That is not his argument. His argument is that agents removed the *reason* you used to hire people you could not fully trust.

You do not have the need to go and hire people that you do not trust, that you do not trust have high agency or the right skills, because you can use an agent to do those things.

Mandatory review is a control designed for a specific organizational shape: a lot of people, unevenly trusted, doing narrowly scoped work handed down from a product manager. Slack's word for that shape is brutal.

They are not automatons that are taking input from a product manager and some sprint and outputting things to a marketing team to go ship.

If agents absorb the work that justified the low-trust hires, the org shrinks to only high-trust people, and the control that existed to police low-trust people becomes pure latency.

He is explicit that this is a bet on a specific failure model, not a universal law.

It is the mean time before recovery versus the mean time before failure approach.

If you can ship a fix 15 minutes after a log line appears, you optimize for recovery. If you cannot, you optimize for prevention, and you keep your gates. He says outright it is "very much not the thing for all software." Anyone quoting him as saying review is dead for everyone is misquoting him.

Local Dev Is Dead, and He Means It Literally

The bigger structural claim in the interview is about where code runs. AMP shipped a thing called orbs: agents that run remotely in a sandbox, with a working dev server you can reach through a portal.

Local dev in our opinion is dead.

The tell was not a metric. It was a personal observation:

I have not run my local dev server in two weeks.

The host, swyx, pushed back with the right historical objection. Big companies have made engineers develop on remote machines for a decade, and engineers hated it, because it was strictly worse than a laptop.

Slack's answer is that agents remove the thing you were giving up:

With agents, when you can run them remotely, you do not feel like you are giving anything up.

That is a real asymmetry. Remote dev used to tax the human in the loop on every keystroke of latency. If the human is no longer in the loop keystroke by keystroke, the tax collapses to near zero. The forcing function was never bandwidth. It was who was typing.

He also notes the adoption curve is not driven by security teams:

It is not your corporate security department that says we need to use VMs for dev. It is you, you kind of just realize.

The Security Inversion Nobody Saw Coming

This is the most genuinely surprising segment, and I think it is the part of the interview that ages best.

Conventional wisdom says a local CLI agent is the safe option because nothing leaves your machine. Slack argues the exact opposite, and the argument is hard to dismiss.

A cloud agent can be handed a scoped, expiring credential. His example is concrete: an OIDC token good for 30 minutes of read-only access to production logs, and nothing else.

A local agent inherits your entire authenticated shell. Every cloud CLI you logged into. Every SSH key. Every token in your environment.

Giving an agent that is running on a developer laptop potentially unfettered access to anything else that the developer might have authorized on their machine.

He cites the Hugging Face incident as evidence that agents escape containment in practice, and says AMP customers have hit the softer version of it themselves, where a stray authenticated script found a path to a production console.

Then he makes the prediction that will either look prescient or absurd:

In two months, we could be seeing CLI coding agents is basically dead.

I think the timeline is wrong and the direction is right. Two months is founder time. But the underlying logic is sound and largely unarguable: when the same capability is available with a smaller blast radius, the larger blast radius stops being a feature and starts being a liability. The reason people cling to local agents is not security. It is that unrestricted access is *convenient*, and every scoping decision requires you to predict your needs in advance.

swyx named that tension precisely, and it is the honest counterargument:

There is a fundamental tension in the UX between you just have everything and I treat you as a full human that is on my team, versus you are still on a tight leash.

CI Is a Wiggly Tooth

If review goes and local dev goes, CI is the next domino, and Slack is openly mid-argument with his own team about it.

His question is genuinely hard to answer. His agent already runs the tests. It runs them in a sandbox that is identical for everyone on the team. That is reproducibility, which is the whole point of CI.

If my agent has run the tests for its own verification, why do I need CI?

swyx gave the standard defense: you do not trust the agent to be exhaustive. It runs what is in its context. On a large codebase it cannot know that a change touched something three modules away.

Slack conceded it, and then immediately undercut the concession:

I am with you maybe for like three more weeks there. I am like a wiggly tooth.

His counter is sharp. Almost nobody actually runs their full suite on every commit. Real CI at scale already uses heuristics, usually running tests only for changed files. So the exhaustiveness argument is defending a property that most CI setups do not have either.

Half the AMP team is like, guys, we do not need CI. We need something that will package the image and then push it for deployment.

That last sentence is the useful one. Strip CI down and what survives is the build-and-push step. The "test gate" half is the part under attack.

And swyx closed the loop with the observation that makes this less a death than a migration:

What is CI but a reproducible environment in which to run your project?

Sandbox companies like E2B and Daytona are growing fast. Something CI-shaped is thriving. The YAML file in your repo is what is dying.

GitHub Dies With a Whimper

The line that will get clipped most:

It is not like AMP is the GitHub killer. It is the next GitHub. These things do not die with a bang. They die with a whimper.

His actual usage report is more damning than any competitive claim:

We are not using issues. We are not using pull requests. We are barely on GitHub actions and we are looking to get off of that.

They keep GitHub to push the repo. That is it. He notes the discomfort of that dependency honestly, saying someone could change it from under them and tell the team a week later.

Look at the causal order here, because it matters. They did not switch off GitHub to be contrarian. They removed mandatory review, which removed pull requests. They removed PRs, which removed the review UI. They questioned CI, which removed Actions. Issues went when the team got small enough to hold context in a chat thread.

GitHub did not lose on features. It lost because the workflow it encodes was disassembled one piece at a time.

The Agent Is the Ultimate Settings Screen

The most portable idea in the interview has nothing to do with git.

Slack demoed an ad hoc internal dashboard someone on his team spun up inside an orb. Two years ago that is a data team ticket and a Looker query. The intermediate step everyone is building right now is to bolt an MCP server onto the dashboard tool so an agent can drive it.

His question is why that intermediate step exists at all.

An agent is the ultimate settings screen for any software, and code is the ultimate settings screen for any software.

Settings screens, integration menus and plugin APIs are all the same artifact: a vendor guessing which parameters you might want to change, then building a constrained UI for that guess. Every one of them is a prediction made in advance, by someone who has never met you.

If the customer has an agent and the source, the guess is unnecessary. You do not configure the software. You edit it.

He is candid that this asymmetry cuts against his own business:

It is so much easier for you to build something that is going to be self-hosted that just has to solve one person's problem. If you are trying to build multi-tenancy, that doubles the complexity.

Multi-tenancy, settings, permissions and integrations are most of what a SaaS product actually is, and none of it is the feature you bought it for. It is the cost of serving strangers. Serve one person and the cost vanishes.

swyx pushed that to its conclusion:

Coding SaaS will be the last SaaS in the world, because all the other SaaS will just be buildable via coding SaaS.

That is too clean to be literally true. Regulated data, network effects and real-time infrastructure are not falling to a weekend build. But the mid-market tool with 40 settings and a Zapier integration should be terrified.

Jellyware, and Why It Is Not Open Source

AMP is planning to release these internal tools, and Slack coined a word for the category.

It is like softer than software, but it is not totally vibe coded, and you can customize it with your own agent.

Jellyware is a distribution model dressed up as a joke. The point is what it lets you skip. Shipping an open source project means maintenance, issue triage and backwards compatibility forever. Shipping a product means settings and integrations and a sales motion.

Jellyware is neither. You publish code that works, and the recipient's agent adapts it. Nobody owes anybody an upgrade path.

swyx has already arrived at the same place from a different direction, saying his published skills are now the bulk of his GitHub usage:

That is probably the new open source, which is you open source some markdown files with some sample code.

I am not fully sold. Jellyware means every consumer maintains a private fork with no shared security patching, which is exactly the npm supply chain problem with the fixes removed. But "artifact plus your agent" as a unit of distribution is real, and it is already happening whether or not the name sticks.

Shipping a Schema Migration From 35,000 Feet

The most concrete evidence in the interview is a story, not a claim.

AMP was migrating from one workspace per user to many. If you have shipped software, you know this shape: a change at the core of the most important data model, staged across many backwards-compatible phases.

Slack ran it from a plane to Munich on airplane wifi.

The loop he describes is the thing worth stealing:

After it deploys a phase, it will go and monitor the logs. It will go and monitor the database to make sure that the invariants that it thought of, to make sure that you are not seeing a bunch of errors that are unexpected.

Deploy a phase. Watch logs and database invariants. Green means advance. Red means roll back.

I had it push out a few stages that were pretty safe and monitor the logs. And if everything was good, then it would go on to the next phase. And if not, then it would roll back.

This is the actual answer to "why does the agent need production access," and it reframes the security debate from the previous section. The agent is not reading prod logs for fun. Log access is what makes the migration self-verifying, and self-verification is what lets the human sleep on the plane.

Note what makes this safe, because it is not the model. It is that the invariants were specified up front and the rollback path was defined before the first phase shipped. That is release engineering discipline, not vibes. Most teams that copy this will copy the autonomy and skip the invariants.

Killing Features Is the Strategy

Underneath all of it is a product philosophy most companies would consider suicidal.

Some people joked, for the last few months we were more known for killing features than for adding features. And I think that was the right decision.

His justification is the sharpest strategic line in the interview:

The value of keeping users on doing the old thing is actually negative.

The reasoning is that technology cycles used to run five to ten years, so acquiring a user meant a decade of retention. That arithmetic is gone.

If you are not nudging them, yanking them to be on the frontier with you, then three months later they are going to say, hey, who are these fools, their product is obsolete.

This only works because AMP deliberately selected for customers who want it. swyx named the tradeoff exactly: the other kind of customer is the one who is trained and certified on your tool and does not want their cheese moved. Both are real businesses. You cannot serve both.

Twenty People, All Co-Founders

The org design is the substrate for everything above, and it came out of a genuinely unusual move. AMP was spun out of Sourcegraph as a separate company, with every investor and employee holding a share of both. Slack notes it is rare, comparing it to Yammer spinning out of Geni around 2009.

Twenty people went with AMP. Everyone else stayed on Sourcegraph.

I could feel totally at ease with any single person on the team, talking to any of our customers, fixing any bug, doing anything.

That sentence is the actual replacement for code review. Not tooling. Interchangeability.

He is dismissive of peers his size who start hiring functional specialists:

I see some other companies that are around our size starting to go hire a PM or a marketer, and that just feels like the old way of building a software business.

His worry is the ratio: 10 percent of people building the product, 90 percent on the overhead of selling it. He grants that split was once necessary. He does not think it is anymore.

There is real evidence he is not alone. swyx brought up Jeff Dean leaving Google, apparently to work with roughly five people, and read the mission statement as a pointed comment about what thousands of people at Gemini could not do that a handful might.

The honest caveat, which Slack offers himself: AMP is profitable, in a fast-growing market, with a historical accident that funded the setup. That is not a reproducible template. It is a set of conditions.

What I Think He Gets Wrong

Three things.

The two-month timeline for CLI agents dying is founder time, not calendar time. Scoped cloud credentials are better security, but the migration requires infrastructure most teams do not have and will not build this quarter.

The trust argument is somewhat circular. Twenty co-founders is not a policy other companies can adopt; it is a starting condition. A 200-person company cannot decide to have 20 co-founders. The interesting unanswered question is whether you can grow from 20 to 200 without reinstalling every gate you removed, and nothing in the interview suggests he knows.

And jellyware without shared maintenance is a security posture, not just a distribution model. Every private fork is a patch that will never be applied. To his credit, Slack does not pretend any of this is settled:

It feels like nobody knows and we are all going to figure it out. That is why the best place to be is just getting out there and building.

None of that touches the core observation, which is the thing worth taking away. Every process in your engineering org exists to compensate for a specific scarcity: scarce trust, scarce reproducibility, scarce attention. Agents changed the price of all three. Most teams have not gone back to check which of their gates were load-bearing and which are now just latency.

Key Takeaways

  • Mandatory review before main is a low-trust control. AMP removed it by removing the low-trust hires, not by trusting the model.
  • Local CLI agents inherit your entire authenticated environment. A cloud agent can be given 30 minutes of read-only prod log access and nothing else. The safe-feeling option is the wide-open one.
  • CI's exhaustiveness argument is weaker than it looks, because most real CI already runs only changed-file tests. The build-and-push half survives; the test-gate half is under attack.
  • Agents make settings screens, integration menus and plugin APIs obsolete for anyone holding the source. Multi-tenancy is the cost of serving strangers, and it is most of what a SaaS product is.
  • Agent-run migrations are safe because of specified invariants and a defined rollback path, not because of model quality. Copy the invariants, not the autonomy.
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Max@maxxrubin_

So Astra is able to identify sounds from mel spectrograms zero-shot. I don't think we've scratched the surface of what this model can do (and this is light reasoning btw)

[TWITTER]
Saved Sep 7, 2026
Max@maxxrubin_

So Astra is able to identify sounds from mel spectrograms zero-shot. I don't think we've scratched the surface of what this model can do (and this is light reasoning btw)

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Trigram-indexed grep with a client/server architecture for fast regex search in large codebases locally

microsoft/tgrepmicrosoft
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Saved Sep 7, 2026

bb: the IDE that builds itself

bb can control, customize, and automate itself, laying the groundwork for your own software factory. Fully open source and local-first, with Claude Code, Codex, Cursor, Pi, OpenCode, Grok, omp, and Hermes.

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