Building digital products end-to-end. Discovery • Strategy • Execution
Joined September 2025
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Bidaï retweeted
Créateurs, entrepreneurs et passionnés de tech, retrouvons-nous en Space pour partager nos projets, nos expériences et créer de vraies synergies. Construisons ensemble !
Qui sera de la partie ? ! teapawt.org/i/spaces/1vJpPNmllnMJE
Bidaï retweeted
1/Un conseil de Manager 🧵 J’ai travaillé dans plusieurs environnements complexes, dans différents secteurs. Une leçon revient toujours :
Personne ne se souvient de ce que vous avez livré pendant votre période d’essai
Mais tout le monde se souvient de ce que vous avez mal compris
Bidaï retweeted
Google's "Attention is All You Need" paper came from trying to get a 3% gain in Google Translate.
Innovation is a consequence of production. "If you don't make the thing, you cede your opportunity to innovate on the thing."
~ Palantir's CTO @ssankar
Bidaï retweeted
A sign of intelligent person is their ability to simplify things, not complicate them.
Bidaï retweeted
“AGI should match the full range of human cognition.”
Demis Hassabis, co-founder and CEO of Google DeepMind, expects AGI around 2030.
The real shift is intelligence becoming a tool that transforms science, economies, and human life.
Its impact will depend on keeping powerful systems open, aligned, and widely distributed.
Bidaï retweeted
“AI will force us to redesign school.”
Demis Hassabis, CEO of Google DeepMind, says AI should teach knowledge while classrooms develop human skills.
Education shifts from standardized instruction to personalized learning, creativity, judgment, and collaboration.
AI does not replace school. It changes what school is for.
Bidaï retweeted
Excellent work from Thinking Machines
Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
thinkingmachines.ai/news/int…
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
Bidaï retweeted
An important piece from Demis. We need more of this kind of thinking. A good reminder that the goal is a frontier ecosystem that promotes innovation and choice, while avoiding any one model drop that breaks the world!
Bidaï retweeted
“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.
Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.
The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!
Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.
The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.
When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.
AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.
External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.
With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!
I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).
[Original text: The Batch]
Bidaï retweeted
At @coinbase our AI spend is down nearly half this quarter while token usage keeps climbing. My team built the infrastructure behind it: routing, caching, cheaper defaults, and the spend services that track it.
We route everything through our own gateway: a single endpoint and format for dozens of models, with cross-provider failover, redaction, logging, and cost controls all applied before anything reaches a vendor.
We started with cheaper defaults and caching. 91% of employees weren't hitting their usage caps. Instead of lowering caps, we set cheaper model defaults to cut spend. Caching took more work to get consistent across every tool and model family. A cache hit needs the prefix to match exactly, so we keep building a long, stable prefix across turns. Each request only pays full rate on the new tokens and reads the rest from cache.
Our routing accounts for caching too. The naive approach scores each turn on its own and sends it to whichever model fits, which seems reasonable but would run up spend. The cache is per-model, so switching mid-conversation invalidates it. Our router weighs cache state alongside how hard the task is: a conversation keeps its model while the cache is warm, and the chance to re-route comes only when it goes quiet long enough for the TTL to lapse. Once it does, the router is free again to pick the best model for the task.
These improvements happened at the gateway, so they apply across every team and tool. Next we're going deeper on the coding harness, where we have the most signal and flexibility, tuning how subagents and context get managed.
How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching.
Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work.
Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task.
Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented.
Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted.
Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect.
The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable.
Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.
Bidaï retweeted
“Patience and aggressive opportunism is what you need. It's an odd combination, but it's what works best.”
— Charlie Munger
Bidaï retweeted
Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses.
trace every run → judge it with an LLM → diagnose → fix → ship.
That loop is how agents self-improve over time.
Agent loops + memory + harness + evals - thats the stack.
Watch it, then save the framework below.
