@machinelearnflxi
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AI engineering, distilled. Agents, LLM systems, MLOps & infra architectures, papers, and tools worth your time.
Italy
Joined January 2016
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Machine Learning FLX retweeted
Job4Fit is worth a look - free while in beta:
• CV analysis with an ATS score and what to fix
• paste a job ad, get red flags, culture signals and growth outlook
• mock interviews with role-specific questions and feedback on every answer
job4fit.com
Machine Learning FLX retweeted
Can an LM, starting from random init (!!), learn to generate all of its pretraining data?
Introducing Self-Play Pretraining with Zero Data. Two models start from random initialization: a generator proposes programs for a universal Turing machine and a learner trains on their outputs. We never train on any real data, but see predictable scaling on natural datasets: zero-shot val loss on images, text, audio, and melodies decreases predictably with self-play compute. And the learner develops in-context learning capabilities.
A fun proof-of-concept, co-led with @AdityaCowsik and @KfirDolev and co-authors @gbruno_dl, @ANourya @noahdgoodman, and @YoavLevine.
Machine Learning FLX retweeted
this is pure f*cking gold for anyone running coding agents
Jev founder Diogo Amogo wrote a PDF on building a Jev harness
the promise:
> 200x faster
> 400x cheaper
the model hasn't been the slow part for a while
the speed and the cost sit in the harness wrapped around it
• how to use it
> drop this PDF and the article below into Claude Code or Codex
> let it rebuild its own setup
one evening of setup
and your agent starts next week on a harness most teams still don't have 👇
Take ML models from training to edge deployment. Diagnose training instability, tune hyperparameters and explain model behavior with SHAP. Cover quantization and deployment with TensorFlow Lite. imp.i384100.net/c/6457882/12… #AD #MachineLearning #MLOps
In 12 weeks, we built a research facility that is run entirely by AI.
AI designs, executes, and observes experiments end-to-end across biology, chemistry, and materials science.
We’re introducing SciUniverse: a benchmark that measures AI’s ability to do real-world scientific research.
Machine Learning FLX retweeted
JUST IN: Warp raises $85M for an AI Head of HR that runs HR Departments completely autonomously: payroll benefit, compliance and IT while monitoring 10,000+ tax jurisdictions and 150+ countries.
Warp claims to have already saved customers $100M+ in penalties.
🤝 Paid partnership
We’ve raised $85M for this moment.
Introducing Warp 2.0: The first AI Head of HR.
Every company is building AI to replace jobs. Warp is building AI to do the jobs no human should have to:
If you work in HR, I want you to spend time with the manager who needs help or building company culture people actually want to work at.
If you’re a founder, I want you to focus on signing clients or spending time with your family.
You shouldn’t have to figure out how to register state tax in California. You shouldn’t have to pay outrageous penalties because you don't know what a DE 9C is.
I want to make HR human again. Today, this is finally possible with the Warp Agent.
I’d love for you to see it in action: warp.co/agent
Machine Learning FLX retweeted
What happens if an AI starts training with zero real-world data?
In a new paper, two models start from scratch. One writes small programs that generate byte sequences. The other learns to predict them. The first model then learns to generate sequences that push the second to improve.
After training only on those generated sequences, the learner became better at predicting real text, images, speech and DNA as training compute increased. It’s an early proof of concept, but a striking result: useful general patterns emerged without training on natural examples.
🤖 Made with AI
Machine Learning FLX retweeted
What would pretraining with zero real data look like? Can a randomly initialized model learn to generate all of its training data entirely through self-play? Find out below! :)
Can an LM, starting from random init (!!), learn to generate all of its pretraining data?
Introducing Self-Play Pretraining with Zero Data. Two models start from random initialization: a generator proposes programs for a universal Turing machine and a learner trains on their outputs. We never train on any real data, but see predictable scaling on natural datasets: zero-shot val loss on images, text, audio, and melodies decreases predictably with self-play compute. And the learner develops in-context learning capabilities.
A fun proof-of-concept, co-led with @AdityaCowsik and @KfirDolev and co-authors @gbruno_dl, @ANourya @noahdgoodman, and @YoavLevine.
Make semantic search with Sentence Transformers. Generate embeddings, calculate cosine similarity, and build question answering systems. Cover API deployment with FastAPI, too. trk.udemy.com/c/6457882/3227… #AD #NLP
Machine Learning FLX retweeted
Opus 5.5 is 20% cheaper per input and output token than Opus 5, and 60% cheaper on cache reads. So what does that actually do to the cost of a task in Claude Code?
We ran the numbers, and built a calculator so you can run yours from /usage:
claude.dev/blog/what-a-task-…
Machine Learning FLX retweeted
It’s now easier to build plugins for Claude.
We built a new portal to submit your plugin, track review, and see usage.
Plugins package MCP and skills, and are becoming the way to build for Claude. MCP usage across Claude products is up 110x this year!
claude.com/blog/build-plugin…
Machine Learning FLX retweeted
You've never routed like this before.
@OpenRouter is bringing Jev to all of your LLM calls, so your agentic workflows never have to waste a token again.
As always, faster, cheaper, more intelligent. Go build the future.
Introducing typesafe/jev-router: a cache-aware model router powered by Jev and @typesafeai
The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost.
Here's how it works 👇🏻
Machine Learning FLX retweeted
Jev, now open source: Lev
A 4B open source System One model based on Qwen backbone
The best performance for it's small size
huggingface.co/interfaze-ai/…
Machine Learning FLX retweeted
This is f*ckin awesome.
A Stanford team pairs JEV with Claude Code to sort 75 billion data points every 11 minutes.
JEV runs a cheap first pass on everything. Claude only gets the hard cases.
Faster, cheaper, way less compute burned.
Machine Learning FLX retweeted
jev and langgraph are a great combo!
modeling agents as complex systems, with AI imbued in them, makes a ton of sense
langgraph is the best way to model them as such, and using Jev inside langgraph to turbo charge all the small decisions that need to be made is awesome combo
I still don't understand why people are still building agents as a straight line
you write "do A, then B, then C." half those steps never needed to wait on the one before them. you just paid for the wait anyway, every single run
one test tells you everything, ninety seconds, before you write a single line of orchestration code:
walk your workflow step by step → ask one question at every seam → does this step actually need the last one's output
if yes → keep the order, it's a real edge
if no → cut it, that wait was fake and cost you nothing but time, on purpose, this whole time
now split what's left into the pattern that actually scales:
fan out → one agent per independent piece, all running at once
reduce → plain code, zero model tokens, just flatten and dedupe
verify → a fresh agent with no memory of the work, one job: try to kill it
synthesize → one final agent writes the answer from what survived
here's the part that breaks every setup people skip:
the verifier can never share context with the worker → give it the same conversation and it's not checking anything, it's agreeing with itself in a different window, wearing a different name
one rule, no exceptions → worker and verifier, separate context, always
most people build the fan-out, call it done, and never notice the thing checking their work has been the exact same hand the entire time
ninety seconds to run the test. you never queue a fake wait again
Take a neural network from setup to deployment. Use TensorFlow for image classification and text generation, progressing from your first network to CNNs, RNNs, and LSTMs. trk.udemy.com/c/6457882/3227… #AD #DeepLearning
Three courses take TensorFlow into real projects. Work on computer vision, natural language processing, and deep reinforcement learning, with guidance on structuring TensorFlow programs. trk.udemy.com/c/6457882/3227… #AD #TensorFlow