Register and share your invite link to earn from video plays and referrals.

Drew Breunig
@dbreunig
Writing about and working on AI, DSPy, geo, and data. Trying to prevent prompt debt and help others build maintainable intelligent applications.
1.2K Following    10.4K Followers
Yesterday I gave a talk on “Context Fails & Fixes”, that functioned a bit on a general “State of Context Engineering”: where it came from, where it's gone, and where I think it's going.
Show more
The legal metaphor for AI engineering (where you write laws, then fine tune them as they meet the real world with case law and precedent) came up several times this week, and I’m liking it more and more.
Show more
This office hours recording from @DSPyOSS is a gold mine of excellent Jev use-cases and explainers!
Here's a repo demoing these compound AI patterns with DSPy + Jev!
@isaacbmiller1 @CompleteSkeptic Here's the post. Really enjoyed these thoughts. It's a great exercise to reconsider the design choices KV caches make for us.
the @DSPyOSS town hall about dspy+jev was really good. here are some of my favorite highlights
Also @hammer_mt will drop by!
Today at 2pm PT, @isaacbmiller1 and I will be walking through @DSPyOSS's Jev/System One implementation & the ReAnchor optimizer. Will also sharing some design patterns for for selective compaction, tool approvals, and subagent delegation using Jev.
Show more
Today at 2pm PT, @isaacbmiller1 and I will be walking through @DSPyOSS's Jev/System One implementation & the ReAnchor optimizer. Will also sharing some design patterns for for selective compaction, tool approvals, and subagent delegation using Jev.
Show more
Someone please exfiltrate the system instructions for
@isaacbmiller1 @CompleteSkeptic Here's the post. Really enjoyed these thoughts. It's a great exercise to reconsider the design choices KV caches make for us.
I wrote a short blog on my experience testing Jev on reasoning-intensive regression problems. The results are within expectations (Jev is focused on classification after all), but the model has great attributes and is wicked fast! Read about it here:
Show more
tbf, in the Claude Code leak we saw Anthropic was using regex for swears to flag threads for QA review.
Jev agrees that I'm vibing more with Opus.
Just released dspy-monty-interpretter, bringing monty 1.0 to all your dspy.RLMs!
Fuck it, still early but here goes ... We've just released Monty v1 - a Python sandbox that starts in 1 millisecond, not 1.5 seconds. I just ran 10k sandboxed scripts in 674ms, something that would take a cloud sandbox > 3 hours. This removes the biggest drawback of letting agents write code. The future is fast. Even better, it's open source, you can install it from PyPI, npm or Crates now. Serviced platform coming soon. Please get in touch if you want to be a design partner! Who should try it? ⚡ if you care about startup time, use Monty ⚡ if you care about long-lived sessions, use Monty - Monty can be dumped and resumed at any external function call ⚡ if you care about accessing functions in the agent/host, use Monty - Monty makes it trivial to expose local functions into the sandbox ⚡ if you care about scale, use Monty - Monty workers use as little as 2MB of memory, meaning you can run thousands of concurrent sandboxes on a single machine ⚡ if you care about security, use Monty - we've run 3 rounds of bounty program and thousands of researchers have tried to break into our sandbox, meaning it should be secure to run untrusted code Who should avoid it? 🚫 if you like to take a coffee break while waiting for sandboxes to start, DO NOT use Monty 🚫 if you enjoy the challenge of routing API requests from sandboxes through your corporate network to access state in your agent without exposing secrets to the sandbox, DO NOT use Monty 🚫 if your agent really needs to install packages from PyPI, Monty won't help you yet (spoiler: it probably doesn't)
Show more
The most recent batch of models start to respond in a limited and localized vocabulary as you go over ~200k context. Unique terms are adopted, more like pointers (symbolic) than jargon (language). At least it feels that way to me. I end up spending significant effort getting them to “decompile” this dialect when writing reports, or even product copy.
Show more
I bet we figure out that neural networks can be decomposed into evolved symbolic systems of sorts and that it will be achieved before capital S Superintelligence but everyone has to lock in
So often, the answer is: an RLM with tools.
What’s your favorite skill you’ve made?
I'm heading to the GitHub HQ this coming Monday night for AI Show & Tell with @isaacbmiller1 from DSpy, @satyapd from Scalekit, @MathisJoffre1 from Blaxel AI, plus my colleague @cedricvidal. RSVP:
Show more
Pay attention to System One models if you are building custom harnesses and agentic workflows. Great to see DSPy integrate Jev and now support System One models for calibrating agentic workflows. I'm amazed at how naturally System One models like Jev fit into these frameworks. I am using Jev to build my own custom harnesses like here: So far, I've had great success with guardrails, routing, and verifiers. However, I am starting to see other interesting applications in other parts of the agent harness, like: > structuring skills and better optimizing them > more efficient tool calling for agents > dynamic workflows (i.e., dynamically generating harnesses with orchestrator + subagents patterns) that leverage Jev-like models for more optimally structuring outputs/information traveling between these workflows > enhance coordination between agents > dynamic and smarter loading (like tools and MCP) of system prompts, and really, all areas of context engineering that benefit from smarter and faster decision-making capabilities > RLM + System One Models And the list goes on and on. Just a wonderful time to explore new ways to work with agents.
Show more
Putting “no eyebrows” in my CLAUDE.md