OpenAI's Astra Solves 10 Decade-Old Math Problems
A research-stage multi-agent system cracked open problems that resisted humans for years — for about $2,000 of compute.
Topic
Agents
LLMs that use tools, take actions, and complete multi-step tasks.
GPT-5.6's Three Tiers and the 'ChatGPT Work' Agent
Sol, Terra, and Luna split the lineup by capability — and an agent that runs multi-hour projects.
Anthropic Reports First Profit Amid IPO Speculation
Profitability plus investor meetings point to a possible public-market debut this fall.
Long-Horizon Agents: AI That Works for Hours
The frontier is shifting from instant answers to systems that grind on a task for hours or days.
Multi-Agent Systems for Genuinely Hard Problems
Decompose, delegate to sub-agents, verify — the pattern behind AI cracking problems a single model can't.
Machine-Verified Proofs: Why They Change Everything
When an AI's answer can be checked by a machine, you don't have to trust the AI at all.
Is AI Doing Real Research Now?
AI systems are solving genuinely open problems. What does that mean — and what are the limits?
Reasoning Models vs Agents: Not the Same Thing
One thinks harder before answering; the other acts in the world. The distinction matters.
How to Pick an AI Model in 2026
Dozens of capable models, tiers, and providers. A practical framework for choosing.
What Comes After the Current Frontier?
Next-gen pretraining runs, multi-agent research systems, and the shape of the coming leap.
The Router Pattern: Triage Cheap, Escalate Hard
The single highest-leverage design for affordable, high-quality AI products.
Neura Robotics Raises a Record ~$1.4B for Full-Stack Robots
One of the largest robotics rounds ever — a sign embodied AI is heating up.
Enterprise AI Agents Go Mainstream
Prebuilt agent catalogs and big-consultancy rollouts signal agents leaving the demo phase.
GitHub Copilot Adds Its First Open-Weight Coding Model
Even the biggest closed coding assistant reaches for open weights.
South Korea Unveils a Reported $880B AI and Chip Plan
A decade-long national bet on semiconductors, AI infrastructure, and robotics.
Meta's Muse Spark 1.1 and Its First Paid API
Meta enters the paid-API game with an agentic, computer-using model.
LLM Routers: The Right Model for Each Query
Not every request needs your best model. A router decides — and saves a fortune.
Mixture-of-Agents: When Many Models Beat One
Let several models draft, critique, and refine — and the ensemble outperforms any single one.
Observability for AI Applications
You can't fix what you can't see. AI apps need tracing, logging, and metrics of their own.
AI Gateways: Running Many Models in Production
As soon as you use more than one model, you need a layer to manage them.
AI Agent Frameworks: The Landscape
Building agents from scratch is hard. Frameworks help — but add their own tradeoffs.
Robotics and Embodied AI: Models That Act in the World
The same AI revolution that transformed text is now reaching into the physical world.
Function Calling vs MCP vs Full Agents
Three levels of letting AI 'do things' — and knowing which you actually need.
Guardrails vs Fine-Tuning for Safe AI
Two ways to keep a model in bounds — one wraps around it, one changes it.
Customer-Support Agents That Actually Resolve
The difference between a bot that deflects and one that fixes the problem.
AI Tutors: Personalized Learning, Explained
The old dream of a patient, personal tutor for everyone — now technically within reach.
Jailbreaks and Prompt Injection, Explained
The two ways people trick AI systems — and why one is far more dangerous.
AI for Legal Work: Contracts and Research
Law is drowning in documents — exactly the problem AI is good at, with caveats.
Kimi K3: The 2.8-Trillion-Parameter Bid for the Top
Moonshot's July release is the largest Chinese model yet — and it's built for agents that don't lose the thread.
AI in Finance: Fraud, Forecasting, and Risk
Finance was doing machine learning before it was cool. Here's where the new AI fits.
Vibe Coding: Building Software by Conversation
The rise of describing what you want and letting AI write the code — and its limits.
GLM-5 Is the Open Coding Frontier
Zhipu's flagship leads open-weight coding — and coding is where agents earn their keep.
Reasoning with Tools: When to Compute, Not Guess
A model that reaches for a calculator beats one that guesses at arithmetic.
Red-Teaming AI: How Models Get Stress-Tested
Before a model ships, people are paid to try to break it. Here's how, and why.
Guardrails for Autonomous Agents
The more an agent can do, the more it needs limits it cannot cross.
Verification Is the Bottleneck in Reasoning
A good verifier multiplies the value of every attempt a model makes.
Sakana's Fugu Is a Conductor, Not a Model
Japan's Sakana AI bets that the future is orchestrating many models, not training one bigger one.
The AI Scientist Reaches Nature
Sakana's system for automated discovery — idea to experiment to paper — crossed a real milestone.
Voice-to-Voice Under 800 Milliseconds
The whole loop — hear, think, speak — now fits inside a conversational budget.
The Model Context Protocol (MCP), Explained
A standard way to plug tools and data into AI — the 'USB-C for models.'
Computer-Use Agents: The State of Play
AI that clicks, types, and navigates a screen like a person — promising, and still rough.
Coding Agents: How They Actually Work
The agents that read, write, run, and fix code — and why coding is where agents shine.
Function Calling: How Models Use Tools
The mechanism that lets a language model reach outside itself and act.
Structured Outputs: Getting Reliable JSON from LLMs
If your app needs machine-readable output, hoping the model formats it right isn't a plan.
How to Build a Chatbot That Doesn't Hallucinate
You can't make a model perfect — but you can build a system that rarely makes things up.
AI Coding Assistants: A Practical Guide
From autocomplete to autonomous agents — how to actually get value from AI in your codebase.
Long-Horizon Planning Is the Frontier
Agents are great at single steps and fragile across many. Fixing that is the game.
Agents Need Memory, Not Just Retrieval
Stateless lookups aren't enough for long-running agents. Persistent memory is the next layer.
Teaching Agents to Retrieve
Instead of hand-writing retrieval logic, train the agent to discover it.
Multi-Agent Systems, Explained
When one agent isn't enough, a coordinator delegates to specialists.
Why Agents Still Fail — and How to Fix It
Most agent failures aren't the model being dumb. They're the scaffolding around it.
RAG Grew a Reasoning Loop
Retrieval stopped being a single step and became think, retrieve, rethink, act.
What Makes an AI Agent Actually Work
Everyone is shipping agents. Most of the difficulty is not the model.
Long-Term Memory for LLMs
Persistent, temporally-chained state — not a fresh lookup every time.