If you want to understand how fast AI is improving, look at intelligence per dollar. 1.5 years ago: o1 Pro: $150 / $600 per M tokens Today: GLM-5.3 Flash: $0.15 / $0.50 That’s a ~1000x collapse in price in under 1.5 years, while GLM-5.3 Flash is more intelligent than o1 Pro.

life has been better ever since i got a 5kt emerald gold pinky ring made leaning into the mob boss aesthetique

alright guys you kinda played me encouraging me to be more subagentpilled forked off like five and they all spun up jobs maxing out the cpus, causing a grinding twenty minute beachball fest we need a local scheduler for agent workloads

typing "do it in a subagent", "do it in a subagent", etc. is getting really fucking annoying should i tell my AGENTS.md to understand this to mean it? go func() { make a worktree for that and do it }()

*on a first date in san francisco* so are you a queue-by-default or a steer-by-default in the agent kinda person?

They allow them a sub agent to focus on the task. Don’t pause your main thread that’s literally turning the experience from active learning to loading screen brainrot.

Another insane way to use Jev: Jev makes it dramatically cheaper to identify which corrections and trajectories from an agent run are reusable across agents. And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses: - Claude Code - Codex - Cursor - OpenCode - Hermes, and 20+ more Beacon by @asymptotelabs continuously captures agent sessions across harnesses and stores their traces in a shared index. I used it with Jev to build a live cross-agent memory workflow. Beacon captures each run as it happens across all harnesses on my machine. Then Jev evaluates whether the trace contains a reusable engineering lesson, and the approved lesson becomes available to the next agent. GitHub repo: github.com/Asymptote-Labs/ag… (don't forget to star it ⭐) Preserving an agent run and learning from it are two different problems. Most traces contain file reads, failed commands, routine exploration, and implementation details that only apply to one task. Passing the complete transcript to another agent adds context without identifying what matters. In this workflow, one agent never receives another agent's raw transcript. It receives only the specific engineering lesson produced from the evaluated trace. A correction made in one agent can then prevent the same mistake when another agent encounters a similar problem. The recording shows the complete workflow I built. If you want to dive deeper into Jev, I also wrote a hands-on guide to building this Jev-style decision path with open models, entirely locally. Read it below.

Opus 5.5 is such a good writer that I really enjoy reading its reports now. Such a huge improvement over Fable.

Non-deterministic rules via decision models are about to radically improve AI codegen. Brace yourself for global desloppification.

First image, increase number of image cuttings allowed up to 15 times. Second, same but also included feedback. Each produced box is checked and asked if correctly on a beetle. If no, repeat cycle, if yes keep the box. Greyed squares, Jev said no beetle. Other squares, Jev said 1 beetle then asked to produce the box. Green dot, ground truth. Pink box, Jev box. Yellow dot, Jev box centroid. Blue dotted box, independent cuts for another test (can ignore here). Overall, the feedback loop helps throw away bad boxes but doesn't help finding more. 1 correct beetle was rejected too. Nice trick! :)