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Packed room for our #KubeCon Japan. #ArgoRollouts with AI Agent orchestration to autofix production issues while safely doing canaries with @kevindubois ありがとうございます
I've been running Gemini 4 for a week and it is comparable with Fable in the benchmarks I tried, website migrations, at a lower price. Good stuff
Carlos Sanchez retweeted
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case).
I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.
We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them:
"/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started"
I imagine we'll have some backend services that could be replaced entirely by this new architecture.
Docker Maven images are now ready for 3.10 on Eclipse Temurin, for Alpine and Ubuntu Resolute. Resolute is also available for all Temurin variants.
Example: maven:3.10.0-eclipse-temurin-27-resolute
Very interesting comparison of the decision models like Jev
Introducing the Decision Index 0.1 ⚖️
a rigorous leaderboard comparing jev with 30+ open weights decision models
35+ benchmarks. asking 130K questions to each model
testing knowledge 🧠, automation ⚙️, understanding 🤔and even creativity 🎨
huggingface.co/spaces/multim…
This is the big question, what will happen to our existing human-machine interfaces
"User interfaces are largely going to go away.
Because the agents speak English, or other languages. You can talk to them. You can say what you want. The UI can be generated.
So I can say, generate me a set of buttons that allows me to solve this problem, and it's generated for you."
- Eric Schmidt is the former CEO of Google.
Today’s websites are designed for humans, with carefully crafted UIs.
But we are probably moving towards UI-less web.
automated bot traffic has already surpassed human activity on the web. with AI agents, this number will explode.
In near future its very plausible that 90% or more of web traffic will be non-human.
Imagine a web with no websites. All your favorite apps are now just databases, queried for information by agents that zip around on your behest.
In a world primarily composed of bots, what happens to websites? Do agents learn to navigate them like a human? Do they go away entirely? 🤔
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Video from 'Peter H. Diamandis' YT channel
Carlos Sanchez retweeted
love jev, but upset it
- isn't open source?
- it lack vision capability?
We fixed all of that, introducing SimpleJev.ai
A fully open source library which takes any HF model and Jev-ify it, with an API endpoint
Now on github, and live in production at @FeatherlessAI
🤔 "Harness choice has little effect on task success rate, but can significantly affect the cost"
"A simple harness can be competitive"
"Models may perform better with other harnesses than with their own. So it turns out that your Claude models may not need Claude Code…"
Does your Claude model really need Claude Code…? 🤔
We evaluate 7 models on Claude Code, Codex, and Pi. Three surprising findings emerge:
1️⃣Harness choice has little effect on task success rate, but can significantly affect the cost
2️⃣A simple harness can be competitive
3️⃣The native harness isn’t always the best.
Millions of people are using coding agents, but the impact of harness choice remains unclear.
(1/n) More details in the thread. 🧵
Lots of interesting takes here. One thing I miss: the perf factory should also address business outcomes (we are doing that with usage data analysis)
Death of the IDE & pull requests. PRs and code reviews are being rethought
Here's what OpenAI's agentic software factory looks like, today.
Details: newsletter.pragmaticengineer…
(thanks to all the OpenAI folks who explained how it works! And Perf Factory looks especially interesting to me)
We are building an agent that “handholds” a change all the way to production Low-risk changes follow an easier path; areas of the codebase can opt in to an agent that will auto-approve low risk PRs, removing human acceptance as a bottleneck and improving velocity.
Tested Jev to see what the buzz is all about and this is what I found for my generative websites case 🧵
TL;DR: it's not a chatbot. It's a "choice machine." And for the right job, it's stupidly fast, cheap, and accurate
No free text generation, ever.
The generative websites demo: xitter.cf/csanchez/status/209483…
