Eneko

builder. products. systems.

[Eneko @iameneko]

@OpenAI Thank you. This was really limiting before.

[Claude @claudeai]

We're extending access to Claude Fable 5 on all paid plans through July 12.

[Eneko @iameneko]

@claudeai YES!!

[Eneko @iameneko]

@Polymarket I still periodically refresh with all my hopes for that Anthropic message saying "we are extending..."

[Eneko @iameneko]

@weaverobotics Those cuts on the video in the middle of actually seeing if it can do anything…

[Eneko @iameneko]

@Rubos_Rex @AnthropicAI Its even worse

[Eneko @iameneko]

@TheAtheistsGod @AnthropicAI Now looks like you cant even code with it. So i guess i will write the story of what could have been done with it hahahah

[Eneko @iameneko]

@AnthropicAI Well, I am crying.

[Eneko @iameneko]

@AnthropicAI If you come up with something like "Available until 12 of July then only through API" i will cry

[Eneko @iameneko]

@TheAtheistsGod @AnthropicAI The window to escape always gets smaller. Terrible.

[Eneko @iameneko]

@TheAtheistsGod @AnthropicAI Good idea. Will use this as prompt. Thanks!

[Eneko @iameneko]

Finally

[Anthropic @AnthropicAI]

We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5.

We'll begin restoring access tomorrow, and will share an update soon.

We’re grateful to our users for their patience, and to everyone who worked with us on redeploying the models.

[Eneko @iameneko]

Crazy times. This looks amazing. I can’t stop thinking about applications of this.

[AI at Meta @AIatMeta]

We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2.

Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication.

We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating.

🧵👇

[AI at Meta @AIatMeta]

We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2.

Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication.

We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating.

🧵👇

[Eneko @iameneko]

This is one of the best things i have seen lately on the token/costs discussion. This type of solutions are the ones that make sense

[Brian Armstrong @brian_armstrong]

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.

[Eneko @iameneko]

@AnthropicAI I got really excited for a second, but i guess this is good progress

[Eneko @iameneko]

New models. Huge apparent improvement. No access :(

[OpenAI @OpenAI]

Introducing a limited preview of GPT-5.6 Sol, our next generation frontier model, as well as GPT-5.6 Terra, a balanced model for efficient, everyday work, and GPT-5.6 Luna, a fast and affordable model for high-volume work.

https://t.co/OoM83SyISN

[Eneko @iameneko]

@ThePrimeagen Well if you work solo… maybe

[Eneko @iameneko]

@notcee_fan Bitcoin price

[Eneko @iameneko]

We reached the ceiling. No more models in a while :(

[Polymarket @Polymarket]

JUST IN: The Trump administration has reportedly asked OpenAI to stagger the release of GPT-5.6 over security concerns.