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if you want to automerge thousands of pull requests , this is how she does it (via @poteto). 809 in june. 10,926 in october. merged prs a month that touch the grok bot team's code. i went through her posts and put the setup in one place. every pr still gets an agentic risk review plus bugbot. low- and medium-risk ones automerge. only higher-risk ones get a person. day to day, in her words, it's just grok bot and cursor projects. she used to have more cursor automations and replaced them with slack bots that have routines. she works on many streams in parallel with pstack: perf, bug fixes, agent-friendly refactors, her own skills, features, ui polish, the harness, release and observability. not from sitting in a review inbox. she's said the speed gains come from parallelizing. a single chat can take longer because of verification. she handholds it less, so she can run several large projects at once. the real setup is in the codebase and the checks around it. she treats the codebase as memory. every agent mistake needs to turn into a constraint. her team built their own framework for grok bot so some anti-patterns become hard to write at all. agents have to verify their own work. they run the app, and can sometimes model it in tla+. they produce screenshots or video that are easier to check than the whole pr. formal verification skills and ordinary run-and-debug skills work together. she calls this an inverse pyramid. push as much as you can into determinism (fuzzing, property tests, formal and runtime verification, stricter languages) instead of spinning up more agent swarms. in her oct 6 post, she tells sandcastle, a grok team bot, to cut. it runs a buildkite dry run, dms every pr author (they can object), kicks the build, and watches it. then it starts a fuzz swarm: usually 10+ agents on grok 4.7 xhigh, some directed, some chaos monkey, using their verification skills and feature map. when something is high priority, she tells sandcastle to @ mention poteto, her engineer bot, which then spins up a cursor project to triage and fix. her team ships a new stable version of the client at least once a day. that's a lot of machinery under "just grok bot and cursor projects."

Hydrogen brings major leaps in intelligence, speed, and reliability: ⚡ Cutting-edge intelligence: Surpasses Argon on the @ArtificialAnlys Intelligence Index at default high thinking (and climbs even higher at xhigh), while finishing tasks faster than competing models in its class. 💻 Cleaner coding & debugging: More than triples 3.8 Flash on Terminal-Bench 4.1, cuts fabricated claims by more than 5X, and adapts cleanly when a tool call fails or a search returns empty. 💼 Top marks on real-world work: Ranks #1# on Vals Tax Agent Bench, ties for #1# on Vals Legal Research, and leads on AutomationBench-AA, Multimodal Knowledge Agent (MKA), and Agents' Last Exam.

The most first-principles talk at #AIECode# came from @CompleteSkeptic — who co-created RLHF/ChatGPT at OpenAI and now builds Jev. His point: today’s LLMs are optimized for assistance (a human in the loop), not automation. “95% Einstein, 5% Mr. Bean” is fine for a chatbot and fatal for a back-office decision. The Bitterest Lesson: compute beats algorithms, data beats compute, and the bitterest part is picking the right task. Jev’s bet: not bigger, not cheaper, but reliable.

Paid vs Free AI Tools 💰 1. Research Paid: Free: 2. Video Paid: Free: 3. Watermark Removal Paid: Free: 4. Image Generation Paid: Free: 5. Presentations Paid: Free: 6. Video Generation Paid: Free: 7. Writing Paid: Free: 8. Design Paid: Free: 9. Automation Paid: Free: 💡 Paid doesn’t always mean better. Sometimes, the free alternative is all you need. Which one would you choose? Follow @abir35627

En lo que va de semana, he pasado de herdr a Orca, después a herdr-app y, finalmente, me he quedado con T3 Codes. Os explico lo que me ha gustado de cada uno, lo que no y por qué me he quedado con el último: Herdr @herdrdev Como tmux, pero para agentes. Es minimalista, muy rápida, personalizable y cumple muy bien con el fin para el que está hecho. Lo malo es que no tiene features que se están haciendo populares como conectarse por el móvil y estás limitado a lo que te dé el harness de CC o Codex. Orca @orca_build Se está haciendo muy famosa rápidamente, tiene un montón de features desde visualización de artifacts, alternar entre subscripciones, conexión desde donde estés con el móvil, conexión con GitHub, Linear y otros para ver issues, automations y una larga lista. Entiendo el hype que hay actualmente, pero en mi opinión y con mi manera de trabajar, creo que es demasiado tosco hasta el punto de que me llega a agobiar. Habrá gente que le guste tener una navaja suiza, pero personalmente soy de herramientas más sencillas. Eso sí, funciona muy pero que muy bien. Herdr-app Hace nada sacaron la versión en app de herdr y le añadía algunas features como poder customizarte el layout y generarte plugins para añadirte componentes como un reloj, un diff, un explorador de archivos o incluso un kanban para ver los agentes. Yo de hecho he hecho un pequeño plugin para tener un panel con algunas funcionalidades relacionadas con git como una vista de grafo con el historial de commits, un panel de unstaged y staged changes, e iconos para que sea un poco más estético. (link: Lo que no mola es que aún está muy verde, las conexiones a entornos remotos por SSH están un poco buggy y que carece de features al igual que su versión TUI. Es más bonito, pero no mejora. T3 Code @t3dotcodes Si no has escuchado hablar sobre T3 es que vives debajo de una piedra. La verdad es que el hype era brutal y decidí que era el momento de comprobar si era merecido, y puedo confirmar que lo es. Tiene las funcionalidades justas y necesarias (conexión con el móvil, alternar entre suscripciones, conexión a entornos remotos por SSH, vistas de terminal, navegador y diffs integrados, etc.), y encima están muy bien pulidas. La única queja que más de uno le pondrá es que no es vista de TUI, si no más bien de chat, como la app de Claude Code. Yo que tampoco soy de usarlo me he acabado acostumbrando e incluso me parece más agradable visualmente. Así que nada, espero que, si estás en este rabbit hole como yo y no paras de probar uno y otro, esta información te ayude a decidirte un poco (por lo menos, hasta que me de por probar otros como o alguna parida nueva.

Starlink operates 11k+ satellites in orbit, providing high-speed broadband to millions of people all around the world. The constellation has been carefully designed with tight station-keeping tolerances and automation that allows Starlink to operate safely.

$2000+ FROM ONE AI WORKFLOW Anthropic engineers Hannah Moran and Christian Ryan just showed how Claude Code can turn complex work into automated workflows. In 24 minutes they demonstrated techniques that can help one person do work that used to require developers, analysts and hours of manual work. Think about the economics. One automation can save a company 20, 50 or even 100+ hours every month. Charge $500. Charge $2000. Charge $5000 for a system that keeps saving them money every month. Claude Code is not just a coding tool. It can become a money-making machine when you know what to automate. Most people will watch the video and move on. The smart ones will build something and sell it.

Most forms feel like work. 😩 Long lists of fields = more drop-offs. 𝐜𝐡𝐚𝐭𝐟𝐨𝐫𝐦 turns forms into conversations. 🤖 Describe what you need and AI builds the questions, logic & branching. 🔹 One question at a time 🔹 AI follow-ups 🔹 Clean data from messy replies 🔹 Webhooks & API 🔹 Unlimited responses on the free plan Build forms people actually finish. #AI# #SaaS# #LeadGeneration# #Automation#

AI Drug Discovery Is Becoming a Bottleneck Trade I get excited when an industry starts going through a real regime change. AI drug discovery (“AIDD”) increasingly looks like one of those moments. Two things are happening: 1/ Upstream: the frontier AI labs are piling in. 2/ Downstream: The supply chain is clearly moving. Supply-chain checks suggest the upstream picks-and-shovels of discovery and early preclinical R&D are starting to feel the increase in experimental volume. DNA → protein → assays → sequencing → automation → preclinical testing Names across that stack include $TWST , @GenScript , $ILMN , $TXG , lab-automation vendors and CROs. $TWST expects triple-digit percentage growth in AI-enabled drug-discovery orders in FY26, and another year of triple-digit order growth in FY27. @GenScript's AIDD business doubled YoY in 1H26. Its current platform advertises industrial-scale validation of 4,000+ designs/day, with integrated sequence-to-data workflows. Our channel checks suggest the ramp is moving even faster: roughly 8,000 designs/day currently, with a path toward ~16,000/day by YE26. --- Why does AI drive more wet-lab demand? 1/ AI makes hypothesis generation almost free → way more shots on goal. The bottleneck is moving from expert-driven design to biological validation. 2/ AI models need continuous experimental feedback — and both good and bad data are useful. Traditionally, only the highest-conviction A+ candidates might get pushed into expensive validation. With AI, even the B/C candidates can be valuable because failed experiments generate training data. @GenScript has said its sequence-to-binding workflow can return data in 4–7 days, and that faster cycle times matter because AI models depend on continuous experimental feedback. @Anthropic is a clean example. @claudeai designed 1,320 protein binders. @adaptyvbio converted those digital sequences into DNA, expressed the proteins and tested binding. Only 354 actually bound. And the 966 failures are not wasted. They are useful negative labels: what does not express, what does not bind, what has poor affinity. Those results help train the next model iteration. 3/ Wet labs are no longer just making drugs. They are making training data. $TWST / @GenScript increasingly look like biological data foundries. $TWST explicitly talks about generating model-ready data from AI-designed sequences. In some workflows, the customer may care less about receiving the physical protein than about getting structured experimental results back into the model. Traditional drug discovery asks: “Does candidate X work?” AI drug discovery also asks: “What can this experiment teach the model?” --- TAM of AIDD If AI is simply a better R&D tool, the relevant spending pool is the $300–400B of annual global pharma R&D. If AI meaningfully increases the number of viable drug programs, the opportunity is larger because it expands downstream demand for DNA synthesis, protein production, assays, and preclinical work. Near term, we can also size demand from AI-company spending. If Anthropic reaches $80B of ARR in 2026 and spends just 1% on AIDD, that alone would imply ~$800M of annual investment. --- Trade setup This is a trade that could have long legs. It’s hard to really stop working until PhaseI/II results (2028+) It smells a lot like the bottleneck trade we just saw in semis: GPUs → HBM → networking → power/cooling. In biology, It basically follows the drug discovery process downstream: AI models → designs → DNA/protein → assays → preclinical capacity. After the upstream picks-and-shovels, animal testing could become the next bottleneck. Monkey prices are already near prior highs and CRO capacity is tight. AIDD pushing more candidates into preclinical development would only add demand. ?? But clinical trials are still the bottleneck? This is the biggest pushback I keep coming back to. No matter how fast discovery becomes, drugs still need to go through preclinical → Phase I → Phase II → Phase III → approval. You still need patients, time and capital. But that doesn’t mean the bottleneck trade won’t work. More viable candidates — especially with higher success rates — still means more demand throughout the development process. And who knows: clinical trials themselves may eventually be optimized by AI. ?? What breaks the trade? Near term, the picks-and-shovels trade breaks if experimental budgets stop growing, AI-generated designs don’t translate into useful wet-lab hits, or capacity catches up too quickly. Longer term, the thesis breaks if AI drugs look great in discovery / Phase I but fail at normal rates in Phase II/III. That is why Phase II matters so much. ?? Milestones Late 2026–2027: first Isomorphic-designed drugs enter human trials; more AI-native programs move into IND-enabling work / tox. 2028–2030: clinical trial results start telling us whether AI-designed drugs actually perform better than conventional drugs. Calling all the "bottleneck bros". :) @jukan05 @zephyr_z9 @aleabitoreddit @ParadisLabs +++ More comprehensive analysis:

Higgsfield just got computer use. With new ChatGPT extension. The full Higgsfield interface is now inside Codex, powered by GPT-6.1 Sol. With full access to your local files and every automation skill. Automate creative workflows end-to-end with Higgsfield computer use, all inside ChatGPT.