I build AI agents and the evals that keep them honest. Solutions architect in London. Built ResumeRadar (7K+ scans) and WorkCrew. Views my own.
London, UK
Joined October 2017
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I'm Shola. Solutions architect in London, and I build AI agents on my own time.
What I've shipped:
• ResumeRadar: free CV checker, 7,000+ scans
• WorkCrew: AI automation for UK small businesses, replacing WhatsApp and spreadsheet chaos with systems that run themselves
• A multi-agent customer-service sample with human handoff, published through AWS samples
Here I post what worked, what broke, and the evals that told me which was which.
Follow along if you build with Codex, Claude Code, Cursor or Grok Bot.
In case you're wondering what this means for you:
On the $100 plan, nothing changes but the name. It's just "Max" now.
On the $200 plan, now "Ultra", the 5-hour cap goes once your account is verified.
Weekly limits stay the same. So you can run longer sessions, but the total you get each week hasn't grown. Pace your big runs across the week.
We’re renaming our Max plans:
Max 5x → Max
Max 20x → Ultra
Max is still $100/month, and Ultra is still $200. If you’re a subscriber, there’s nothing you need to do.
We’re also removing the 5-hour limit for verified Ultra accounts, starting today with most existing subscribers. Newer accounts keep a temporary limit until they’re verified, which helps us prevent fraud and abuse. You'll get an email when it's removed.
Weekly limits remain unchanged. View our plans here: claude.com/pricing
If your agent reads big PDFs or runs long computer-use sessions, you were probably hitting the 32 MB cap well before the context window ran out.
Now you get about 4x more room before you have to split the job up.
Open weights at the top of FrontierCode for open models is a big one for teams that can't send their code to a hosted API 👀
Today, we release the weights of a new and improved Le Chonk, advancing the open frontier for agentic coding and cybersecurity.
💻 Built to build: Le Chonk ranks #1 on @cognition’s FrontierCode among open models, and sets a new benchmark for Pareto efficiency on @artificialanalysis.
🛡 Built to defend: State-of-the-art in cyberdefense, put through the paces by the world's leading cyber vendors, including @CrowdStrike, @cloudflare, @rubrik, @zscaler, and others.
Built for sovereignty: Make it yours. Download the weights on @huggingface , customize it with Mistral Forge, and deploy it wherever you need it.
Read more from our team and customers 👇:
In case you're wondering what this means for you:
If you're on Pro 100, 200 or 500 or Business Premium, your usage limits double from today. Pro 200 isn't being reduced as planned, so Work and Codex usage stays the same.
If you've been rationing long agent runs, now's the time to plan them again.
Day 25/
We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today.
We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same. We’re also going to continue offering extended limits for dots for now, and usage of your dot will still continue to be separate from your default usage.
And we'll of course also keep shipping better models and other efficiency improvements that increase your effective usage over time.
Most customer-service agent demos skip the bit where a human takes over.
I built mine around it, on my own time, then published it through AWS samples. Here are the decisions behind it, including the ones I reversed 🧵
When the escalation agent can't help, it opens a ticket and passes the customer to a human queue.
I built that handoff on purpose. It's part of the design.
The full write-up and the code are in the reply below.
If you pay for ChatGPT Pro or Business, you can now claim up to $500 a month in API credits to build apps and agents.
That's the gap between chatting with a model and building with one. If you've been meaning to build your first agent, the cost excuse just got a lot smaller 🙂
In case you're wondering what this means for you:
Gemini 4 Carbon is Google's new top model, two weeks after Argon. The headline is long-horizon tasks, meaning it can stay on a job for longer without losing the thread.
If you run agents, that's the part to test. A longer run means more steps where it can act on a wrong assumption.
Before you switch, rerun your own evals on your real tasks. A leaderboard rank tells you how it did on someone else's test, not yours.
In case you're wondering what this means for you:
Anthropic gave 50 expert analysts an agent that was secretly leaning one way on four judgment calls. They went with its lean 86% of the time.
If a human sign-off is your safety check, it's weaker than it looks. Ask the agent to show the options it didn't pick, and make the big calls before you read its answer.
In case you're wondering what this means for you:
Genie 4 is a world model. You move through a scene in real time and it responds: the sound changes, and the people in it move, react and speak.
Most of us won't ship with it this week. The bigger shift is for agents. They can start being tested in worlds that answer back, not only on fixed benchmarks.
Worth watching if you build or evaluate agents.
In case you're wondering what this means for you:
If you build agents, the headline isn't "agents as code". It's that evals now come in the box.
Before you ship one, write 10 test cases for the moments it could go wrong: the wrong tool, the missed handoff, the confident wrong answer.
Run them on every prompt change. That's how you know a change made it better, not just different.
In case you're wondering what this means for you:
Cheaper computer use makes the boring work worth automating. Legacy apps with no API, form filling, copying data between systems.
In case you're wondering what this means for you: a model that stays on top for a month is your window to build on it properly.
Pin the version, write a few evals for your real tasks, and when the next "best model" drops you'll know in an hour if it's actually better for you.
One month on, and Opus 5.5 is still the model to beat.
In case you're wondering what this means for you: "the same quality for 40% less" is the bit that matters. That's the difference between an agent you demo and an agent you can afford to run every day.