Solutions

AI platform agnostic builder

[Solutions @solutions]

@OpenAI Claimed

[Solutions @solutions]

@OpenAIDevs @NotionHQ @v0 @AmpCode @kilocode @warpdotdev @NousResearch @openclaw @opencode Nice

[Solutions @solutions]

@OpenAIDevs Yes 👊🏼

[John Schulman @johnschulman2]

Proud to release our new model, Inkling 2 Small. Check out our blog post -- it includes a lot of detail about our training process.

To me, the most interesting part of this release was what we learned about alignment, which you can read about in the sections "Safety and alignment" and "Training Inkling 2 Small".

We experienced some unpleasant surprises when our early checkpoints took unexpected actions to circumvent guardrails during training and evals. We realized our capabilities-focused RL was training them to be extremely persistent and to succeed at tasks at all costs. Offline monitors first flagged this behavior, but later we realized it was happening frequently and was easy to replicate.

Before this release, we sprinted on a range of alignment interventions, from SFT to RL. These are briefly described in the blog post. We made a lot of progress, with big improvements on our own alignment evals and on previously unseen evals from external evaluators like Apollo and Goodhart.

We have a long way to go in developing alignment techniques that keep pace with the increases in model intelligence and training scale. We hope to share these techniques to improve the ecosystem. We also have a business reason: our goal with Tinker is to enable many people to do their own large-scale post-training, and we need to help them to do this without incident. Join us if you'd like to build this public science and practice of alignment. https://t.co/LdjsDFRZIN

[Firecrawl @firecrawl]

Introducing headless Clay for agents.

→ Search 250M+ people & 26M+ companies
→ Get emails, job & company data
→ 1000 credits/month free
→ 88% cheaper than Clay at scale

Run all of your enrichment flows in ChatGPT, Claude, Grokbot, and beyond!

https://t.co/pVrxIyAENy

[Solutions @solutions]

@firecrawl This

[Solutions @solutions]

@guillewrotethis @github Thought that’s what we’re doing once we learned about agents anyway

[Laurent @L4UR3N7]

🚨🚨🚨 ALERTE SÉCURITÉ MAXIMALE ! Python est compromis.

🔸 Mettez immédiatement pip à la version 26.2.2 ou plus.
🔸 Dans chaque projet Python (chaque venv) : python -m pip install --upgrade pip
🔸 uv : uv self update (0.13.4 ou plus)
🔸 Mettez aussi à jour les certificats du système (apt / dnf upgrade ca-certificates) et vos images Docker

pip install est compromis.

L'autorité indienne eMudhra (emSign), qui signe les « passeports » prouvant qu'un site est bien celui qu'il prétend être, utilisait des clés secrètes ridiculement faibles : 8 clés, toutes inférieures à 256. L'une valait 4. Autrement dit, n'importe qui peut fabriquer un faux passeport valide pour n'importe quel site.

Le risque pour Python : quelqu'un sur ton réseau (wifi public, FAI, État) se fait passer pour https://t.co/KxkevWMTiB, et ton « pip install » télécharge un paquet piégé sans aucune alerte. Aucune attaque n'a été signalée pour l'instant, mais c'est trivial à faire.

Un paquet piégé peut installer n'importe quoi. Porte dérobée, virus, vol d'informations en tout genre.

La situation est terrifiante !

Rien que dans l'écosystème Python, chaque installation de CPython, chaque venv, uv, tout est exposé.

Et je ne vous parle même pas de npm, pnpm, yarn, Go, Rust.

Ma stratégie de sécurité 2026 : rester chez moi et ne me connecter à Internet que depuis un FAI de confiance.

C'est quand il y a de la mer qu'on reconnaît les vrais marins...

[Solutions @solutions]

@OpenAIDevs Nice

[Solutions @solutions]

@miramurati Open source request

[OpenAI Newsroom @OpenAINewsroom]

We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work.

At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated.

We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.

[OpenAI Developers @OpenAIDevs]

You can finally close your laptop now and your agents will keep working.

Codex cloud environments are here.

Reusable environments mean less setup and faster starts, with your repo, dependencies, scripts, and settings already in place.

[Solutions @solutions]

@OpenAIDevs @thsottiaux Nice

[Sam Altman @sama]

Dots are here!

A new way to use AI that works 24/7 for you; get more of your time and attention back to work at a higher level.

https://t.co/Kp4fPSGUTT

[Anders Lie @anderslie]

How is Jev so much faster than an LLM?

It's not about how the model is trained - it's the inference technique.

In fact, you can modify an inference engine to provide a performant Jev-like API with any open-weight LLM.

Say you are trying to ask N multiple choice questions in parallel.

In standard text generation, you decode tokens one at a time to generate a JSON representing those properties, and then parse the JSON.

In a Jev-like system, what you can do is:
- Label choices for each property 1,2,3,...
- Prefill the shared information (output shape and other context)
- Fork this inference context for each property, re-using that cached prefill
- Add an individual question to each parallel context
- Look at the logits (token probabilities) for each one, constrained to the valid choice labels
- Map the labels back to the choice values. Include the softmax of the logit itself to get a combined choice+confidence score

This means you decode one time in parallel across all questions, instead of one at a time - so the efficiency gains roughly scale with the number of questions per request (until compute is saturated).

So what makes Jev's model unique? It's because they train a small model to perform well in this specific inference setting - improving both cost and capability compared to a non-specialized model.

Ultimately though, if you have a use case Jev would be good for, you could do it with open-weight models too!

(Some speculation made here about how Jev's engine might work, but this is one way you could get similar performance)

[Solutions @solutions]

If you don’t know about this model, you should.

[Hasan Toor @hasantoxr]

A 744 billion parameter AI model just ran on a machine with 25GB of RAM.

No graphics card.

The tool is called colibri.

GLM-5.2 is a mixture-of-experts model. It contains 744 billion parameters but only about 40 billion wake up for each token.

colibri keeps the 9.9GB core in memory.

The remaining 370GB sits on an SSD. As the model writes, colibri pulls in only the tiny group of experts needed for that token.

→ Pure C
→ Zero runtime dependencies
→ 25GB RAM
→ Zero graphics cards
→ The full model stays on your machine

The catch is brutal.

A cold token can require 11GB of disk reads, so output crawls at roughly one token every 10 to 20 seconds. You will not replace ChatGPT with this tomorrow.

But colibri cracks the assumption that the whole model must fit in memory before it can run.

100% open source.

https://t.co/dSm86eRWCj

[Mira Murati @miramurati]

An important conversation about where AI is going and the work that is cut out for us.

[Solutions @solutions]

@miramurati Thank you Mira

[Solutions @solutions]

@ChatGPT Yes I’ve used sites and am one of your 5M 👊🏼

[Jacob Coxon @hilbertspaess]

I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.