Doug Lance

Compaction is a mistake. When an agent's context fills up, tools like Codex and Claude Code compact it: 1. The model prefills the entire session again 2. It writes a summary of everything so far 3. A new session starts from that summary I measured this on a real ~400k-token session with Opus 5.5: → 38 s and $1.99 when the prompt cache has expired → 29 s and $0.17 when it hasn't And the summary is lossy. The exact error message, the file path, the one number you needed is often gone. So I do something new for Mainplane Context retrieval works the other way around. Mainplane keeps the full record of every session as a file. When the context fills up, a new session starts right away with one instruction: "You are continuing session X. Retrieve key context from it as you go." The agent searches the old session and copies the exact lines it needs, when it needs them. Same session, same model: 15 s and $0.10 to get back to work. It does not depend on a cache hit, and nothing is paraphrased. This is the bitter lesson applied to agent memory. Don't hand-design what the model should remember. Give it the full record and good tools, and let it decide. Mainplane is open source (MIT). Link in the first comment.

I no longer know how many agents I have running at any given time

The biggest misread of anyone predicting the end of software engineering was imagining that the world only needed a certain amount of software, and bringing automation to the field would reduce the need for labor. Exactly the opposite is happening where essentially most things are actually turning into software. Because agents can use tools, work with computers, write software on the fly, and process data like humans (but far faster), we can now throw “software” at problems that never could reasonably be digitized before. What that leads to is the role of engineering taking on greater importance in the economy, because we can now engineer far more systems and processes than were possible before. If you’re a bank trying to do new forms of trading, engineers are often your first bet. If you are in life sciences, you expect more and more research and lab work to be engineering centric. If you’re in manufacturing, you’re trying to automate more of the supply chain, all that is engineering. More and more work is shaped like software problems. Yes we’re not going to be coding manually in the future. But almost everything will be code, which means those that understand it are in a fantastic position.

A financial column published in South Korea now warns of memory oversupply in 2029, after new capacity comes online. They argue Korea has only 2-3 years to develop new growth industries before the cycle potentially reverses. $DRAM $MU $SKHY $EWY