Harmonic by the founder of Robinhood dropped how they got a gold medal at the IMO 2025, the elite math contest.

4 teams have done this.

Harmonic Aristotle, unlike OpenAI and DeepMind, uses formal Lean-based search methods and a geometry solver like Bytedance SeedProver.

Google introduced CodeMender: a new AI agent that uses Gemini Deep Think to automatically patch critical software vulnerabilities.

It checks whether its patches are functionally correct, can fix the root cause and doesn't break anything else. This ensures that only high-quality solutions are sent to humans for review.

CodeMender has already created and submitted 72 high-quality fixes for serious security issues in major open-source projects.

It can instantly patch new flaws as well as rewrite old code to eliminate entire classes of vulnerabilities – saving developers significant time.

Life from OpenAI’s Dev day

The gap between open and closed models are narrowing and this trend to continue.

As foundation models become commoditized on a global level, the most interesting directions from both research and commercial is not in their development but in finding new ways to use them.

On the Terminal-Bench Hard evaluation for agentic coding and terminal use, open-weights models such as DeepSeek V3.2 Exp, Kimi K2 0905, and GLM-4.6 have made large strides, with DeepSeek surpassing Gemini 2.5 Pro.

These advances reflect significantly higher capability for use in coding and other agent use cases, and developers have a wider range of model options than ever for these applications.

Fantastic paper “Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning”

RL has long been the dominant method for fine-tuning, powering many state-of-the-art LLMs.

Methods like PPO and GRPO explore in action space. But can we instead explore directly in parameter space?

YES. Researchers propose a scalable framework for full-parameter fine-tuning using Evolution Strategies (ES).

By skipping gradients and optimizing directly in parameter space, ES achieves more accurate, efficient, and stable fine-tuning.

GitHub.

OpenAI DevDay 2025.
Highlights:

OpenAI grew from 2 million weekly developers and 100 million weekly ChatGPT users in 2023 to 4 million developers and 800M+ weekly ChatGPT users in 2025.

The platform now processes over 6 billion tokens per minute on the API, up from 300 million tokens per minute in 2023.

Apps inside ChatGPT

- OpenAI launched the Apps SDK in preview, built on Model Context Protocol, enabling developers to build real apps inside ChatGPT that are interactive, adaptive, and personalized. Docs.

- Launch partners include Booking, Canva, Coursera, Expedia, Figma, Spotify, and Zillow, with apps available today to all logged-in ChatGPT users outside of the EU on Free, Go, Plus and Pro plans

- OpenAI will support many ways to monetize including the new Agentic Commerce Protocol that offers instant checkout right inside ChatGPT

- Later this year, OpenAI will begin accepting app submissions for review and publication, launch a dedicated directory where users can browse and search for apps, and launch apps to ChatGPT Business, Enterprise and Edu (OpenAI expects to bring apps to EU users soon).

Building agents

- AgentKit includes Agent Builder (visual canvas for creating multi-agent workflows with drag-and-drop nodes, available in beta),

- ChatKit (toolkit for embedding customizable chat-based agent experiences, generally available starting today)

- expanded Evals capabilities (datasets, trace grading, automated prompt optimization, third-party model support)

- Connector Registry (beginning beta rollout to some API, ChatGPT Enterprise and Edu customers with a Global Admin Console) consolidates data sources into a single admin panel across ChatGPT and the API, including pre-built connectors like Dropbox, Google Drive, SharePoint, Microsoft Teams, and third-party MCP servers

- Guardrails is an open-source, modular safety layer that helps protect agents against unintended or malicious behavior, available to mask or flag PII, detect jailbreaks, and apply other safeguards

Writing code

- Codex is officially out of research preview and into general availability with new Slack integration, Codex SDK, and admin tools including environment controls, monitoring, and analytics dashboards

- Starting October 20, Codex cloud tasks will begin counting towards usage limits (Plus: 30-150 local messages or 5-40 cloud tasks every 5 hours, Pro: 300-1,500 local messages or 50-400 cloud tasks every 5 hours, with code review not counting toward limits for a limited time).

API updates

- gpt-5-pro (gpt-5-pro-2025-10-06) is now available in the API ($15 per 1M input tokens, $120 per 1M output tokens) for tasks in domains like finance, legal, and healthcare where you need high accuracy and depth of reasoning

- gpt-realtime-mini (gpt-realtime-mini-2025-10-06 - $0.60 per 1M text input tokens, $2.40 per 1M text output tokens, $10 per 1M audio input tokens, $20 per 1M audio output tokens) is 70% cheaper than the advanced voice model with the same voice quality and expressiveness

- gpt-audio-mini (gpt-audio-mini-2025-10-06 - $0.60 per 1M text input tokens, $2.40 per 1M text output tokens, $10 per 1M audio input tokens, $20 per 1M audio output tokens) provides cost-efficient audio processing

- sora-2 ($0.10 per second for 720x1280 or 1280x720) and sora-2-pro ($0.30 per second for 720x1280 or 1280x720, $0.50 per second for 1024x1792 or 1792x1024) are available in preview in the API with the ability to pair sound with visuals including rich soundscapes, ambient audio, and synchronized effects, plus control over video length, aspect ratio, resolution, and the ability to easily remix videos

- gpt-image-1-mini ($2 per 1M text input tokens, $2.50 per 1M image input tokens, $8 per 1M image output tokens, $0.005-$0.015 per image depending on quality and size) is 80% less expensive than the large model

Excel Add-in with Claude AI integration

Take actions in Excel - Build financial models, Analyze customer behavior, Transform messy data.

Now available for max plan users.

Google expanded access to 15 new countries so more people can build AI-powered mini-apps — no code required.

Also launched new features like advanced debugging and a faster building experience.

Wow! Researchers introduced a new RL algo to train agents who can build other agents

Weak-for-Strong (W4S): Training a Weak Meta-Agent to Harness Strong Executors.

With this, SLMs become powerful meta-agents that manage frontier LLMs in diverse agentic tasks.

Code.

Anthropic is preparing Claude Code to be released on the mobile app

It now runs on Anthropic infrastructure not just on GitHub anymore.

Users will be able to connect Claude app to GitHub and run their coding prompts on the go.

Google introduced Gemini 2.5 Computer Use

- Control UIs based with vision understanding and reasoning
- Use for web and Android control
- Try it with Browserbase or locally

MIT Media Lab introduced NeuroChat: neuroadaptive chatbot that adapts its responses to your cognitive engagement.

NeuroChat is the first to use generative AI to adapt on the fly.

Every response — tone, depth, pacing — is co-authored by your brain and the model.

By reading brain signals in real time, NeuroChat personalizes its teaching style to your attention, curiosity, and focus.

Here’s how it works:

NeuroChat measures real-time brain activity using EEG - a lightweight, noninvasive sensor that captures your level of engagement while you learn.

The chatbot uses this engagement score to adjust how it teaches - simplifying, deepening, or changing pace to match your focus.

A live feedback loop between your mind and the model.

NeuroChat isn’t mind-reading.
It tracks only engagement signals, not specific thoughts, memories, or emotions.

All processing happens in your browser, and it’s compatible with local AI models, keeping brain data private.

Researchers ran a pilot study (n = 24) comparing NeuroChat to a non-adaptive chatbot:

1. EEG engagement: significantly higher with NeuroChat
2. Self-reports: users described it as more human-like, fluid, and enjoyable
3. Learning: similar short-term scores, but stronger sustained focus and curiosity

Google shared a new paper, which provides an extremely sample-efficient way to create an agent that can perform well in multi-agent, partially-observed, symbolic environments.

The key idea is to use LLM-powered code synthesis to learn a code world model (in the form of Python code) from a small dataset of (observation, action) trajectories, plus some background information (in text form), and then to pass this induced WM, plus the observation history, to an existing solver, such as (information-set) MCTS, to choose the next action.

Researchers applied their method to various two-player games (with both perfect and imperfect information), and show that it works much better than prompting the LLM to directly generate actions, especially for novel games.

In particular, we beat Gemini 2.5 Pro in 7 out of 10 games, and tie it in 2 out of 10 games.

Anthropic just now introduced Claude Code Plugins in public beta.

Plugins allow you to install and share curated collections of slash commands, agents, MCP servers, and hooks directly within Claude Code.

To get started, you can add a marketplace using: /plugin marketplace add user-or-org/repo-name.

Then browse and install from the /plugin menu.

Try out the multi-agent workflow we use to develop Claude Code:

/plugin marketplace add anthropics/claude-code
/plugin install feature-dev

Anyone can host a marketplace or make a plugin. All you need is a git repo with a .claude-plugin/marketplace.json file.

Google introduced Gemini Enterprise with Agents, Connectors and Agent Builder

“It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context.“

AI-Driven Research for Systems released “Barbarians at the Gate: How AI is Upending Systems Research”

Researchers show how AI-Driven Research for Systems (ADRS) can rediscover or outperform human-designed algorithms across cloud scheduling, MoE expert load balancing, LLM-SQL optimization, transaction scheduling, and more — all within hours and under $20.

Code.

Google gaining search share two months in a row.

Overall search instances accelerating from 3% YoY growth in October 2024 to 27% in September 2025 as GenAI expands the market.

Funny relative to the hysteria earlier this year.

Elon Musk’s xAI is developing world models which are AI systems capable of understanding and designing physical environments.

To achieve this, xAI has hired former Nvidia specialists.

These world models are considered a more advanced form of AI than the LLMs trained primarily on text data, and are expected to surpass the limitations of popular AI tools such as ChatGPT and xAI’s own Grok.

xAI plans to apply world models first in the gaming sector.
The models could be used to automatically generate interactive 3D environments and potentially be applied to AI systems for robotics.

Some tech companies expect world models to become a next-generation core technology that could extend AI applications beyond software into physical products, such as humanoid robots.

Last month, Nvidia told the Financial Times that the market potential of world models could be nearly as large as the entire global economy.

Musk also reaffirmed his earlier goal by posting on X that xAI will release a “great AI-generated game” by the end of next year.