AI Daily Digest — Oct 26, 2026

TL;DR

5 takeaways

OpenAI launches GPT-6.1 Terra, a fast computer-use model it prices at $1 per million input tokens and $5 per million output, 50% below GPT-5.6 Terra; also live on Amazon Bedrock and GitHub Copilot
Sources: OpenAI
OpenAI says its models proved Sogge's 1991 local smoothing conjecture in every dimension and refuted the Exponential Time Hypothesis, with Lean proofs posted and outside expert groups set to examine the results
Sources: OpenAI
Mirendil unveils Fern 0.1, its first model, pretrained from scratch with a context window of up to 10M tokens; access is listed as coming soon
Sources: Mirendil
Short: Anthropic files its S-1 for a Nasdaq listing under ANT
Sources: Anthropic
Short: Meta plans to raise up to about $95 billion through stock offerings to fund AI infrastructure
Sources: Meta

Story 01 / 03

What happened: OpenAI released GPT-6.1 Terra on October 26, a fast model aimed at visual reasoning, computer use and writing. It is available to Plus, Pro, Enterprise and Edu users in ChatGPT Work and Codex, and in the API as gpt-6.1-terra at $1 per million input tokens, $0.05 cached and $5 output. AWS made it generally available on Amazon Bedrock the same day, and GitHub added it to Copilot with a gradual rollout.
Why it matters: OpenAI says Terra scores 15 points above GPT-5.6 Terra on its Agents' Last Exam at medium effort for about a third of the cost, solves 50.3% of Terminal-Bench 4.0 tasks versus 21.5% for its predecessor, and cut its internal error rate at low effort from 53.6% to 16.7%. OpenAI still recommends GPT-6.1 Sol and GPT-6 Astra for the hardest coding work. These are OpenAI's own figures.
Enterprise impact: Form filling, invoice entry and other high-volume computer-use work gets cheaper, and Bedrock availability lets AWS customers use it under existing controls.
Startup impact: Agent products built on screen interaction can revisit unit economics at the new price point.
What to focus on: Independent benchmark results and real-world reliability on long computer-use tasks.
Sources: OpenAI(opens in a new tab)AWS(opens in a new tab)GitHub Changelog(opens in a new tab)

Comment with your GitHub account. One thread per story.

Story 02 / 03

What happened: OpenAI announced three results it says its internal models produced. In math, it says a proof of Sogge's 1991 local smoothing conjecture in every dimension, which would also settle the Kakeya and Stein restriction conjectures; the output came on October 18 and was formalized in Lean the next day. In computer science, it says the Exponential Time Hypothesis is false, also checked in Lean. In physics, it says an existence proof for quantum chromodynamics and related theories.
Why it matters: OpenAI is trying a new release model: outside experts act as shepherds. A group led by Russell Impagliazzo, Ramamohan Paturi and Ryan Williams will examine the ETH result, and a group led by David Gross will handle the physics work. The math output and Lean proofs are public on GitHub. The community has not yet confirmed the results.
Enterprise impact: If the ETH refutation holds, it reopens questions about how fast some hard search problems can be solved, though OpenAI notes practical gains need much more research.
Startup impact: Machine-checked proofs in Lean are becoming the standard for credible AI research claims.
What to focus on: What the expert working groups conclude, and whether the Lean statements match the original conjectures.
Sources: OpenAI(opens in a new tab)openai/math on GitHub(opens in a new tab)

Comment with your GitHub account. One thread per story.

Story 03 / 03

What happened: Mirendil introduced Fern, its first model, on October 26. The company says Fern was pretrained from scratch and supports up to 10M tokens of context. Fern 0.1 is listed at 0.1 to 0.5 trillion parameters, takes text and code, and targets high-volume agentic tasks. Larger Mirendil-Base-Large and Base-XL models are still in pretraining.
Why it matters: Mirendil argues that context length, not only parameter count, is a way to spend compute on capability. Access is listed as coming soon, with an early access request form.
Enterprise impact: Very long context could let agents work over whole codebases or document sets without retrieval pipelines, if quality holds at that length.
Startup impact: A new lab entering with a context-first bet adds another option for long-horizon agent work.
What to focus on: Independent tests of quality across the full 10M window, and when access opens.
Source: Mirendil(opens in a new tab)

Comment with your GitHub account. One thread per story.

Anthropic files S-1
Anthropic filed publicly for an IPO and plans to list on Nasdaq as ANT; share count and price are not set. The filing shows $16.29B in revenue for the first half of 2026, up from $1.16B a year earlier.
Anthropic(opens in a new tab)
Meta's AI fundraising
Meta launched about $35B of stock offerings and plans an approximately $60B at-the-market program later, saying the money supports AI infrastructure and personal superintelligence. It expects $135B to $145B of 2026 capital spending.
Meta(opens in a new tab)

Cheaper agents, bigger claims

Terra pushes the price of computer-use agents down, while OpenAI's research claims will be judged by outside experts, not press releases.