Joe Schmid @jschmid

Forward Deployed Engineering @ https://t.co/rij9dk4elc driver.ai Pittsburgh, PA Joined October 2007

Tweets

238
Followers

317
Following

2K
Likes

661

Joe Schmid @jschmid

3 weeks ago

"We'll just build it ourselves" is the most expensive sentence I hear from prospects. Then, teams come up against the Context Chasm. The pattern is consistent. Teams burn months, and they lose their best engineers to the work. Most never ship the build at all. Context infrastructure works the way version control works. You adopt it. You don't build it. Adam's piece on why: driver.ai/blog/diy-conte…

0 0 1 13 0

View Details

Joe Schmid @jschmid

3 weeks ago

0 1 2 21 0

View Details

Joe Schmid @jschmid

a month ago

Matt Nassr at Optiver described a pattern we keep hearing from engineering leaders. His researchers and traders got dramatically more productive once AI entered the workflow. They consumed pipelines faster, asked sharper questions, ran more experiments. Nobody planned for who would answer all those questions. The questions were reasonable. Does a feed exist for this instrument? What fields does this market dataset expose? Can this pipeline support intraday resolution? Context questions, not research problems. The kind a well-indexed system should answer on its own. The system couldn't. So the questions routed to the engineers who built it. Senior people who should have been designing next-gen infrastructure were burning real chunks of their week on lookups. The AI agents were supposed to handle this. They didn't have reliable context, so they punted humans back into the loop. Context infrastructure isn't a dev tools problem. It's an org design problem. When your best engineers are the indexing layer for your codebase, the company doesn't scale. It just gets more expensive. Once Driver gave Optiver's agents a deterministic view of the codebase, the questions stopped routing through people. driver.ai/blog/optiver-c…

0 1 0 38 0

View Details

Joe Schmid @jschmid

a month ago

@nitmusai That's well said. Our product keeps context up to date with every push to remote and on every active branch in all repos. When AI coding agents ask for task-specific context from our MCP server, we assemble dynamic context from that up to date understanding of customer codebases.

1 0 0 16 0

View Details

Joe Schmid @jschmid

a month ago

A pattern we kept seeing before we built Driver, that I think most engineering leaders will recognize: A team takes the reasonable approach to giving their AI coding agents context. Export the codebase docs to markdown, check them into the repos, point agents at the right directories. At @OptiverGlobal, the team was shipping 120+ commits a day. By the time a markdown file describing a pipeline was reviewed and merged, the content was already stale. The docs weren't wrong because anyone was careless. They were wrong because the code moved faster than documentation could keep up. The bigger problem wasn't the staleness. It was the hidden tax. Engineers weren't just writing docs, but rather they were maintaining them, explaining to agents why the docs disagreed with reality, then re-prompting when the agent got confused anyway. Documentation stopped being an asset. It became a liability. The lesson: if humans have to keep the context fresh by hand, the context will always lag the work. Automating coding agents is the easy part. Automating the context they need is the harder one. That's what we've been heads-down on.

2 1 3 222 1

View Details

Joe Schmid @jschmid

2 months ago

If you're using an AI coding agent like Claude Code, Cursor, Codex, etc. with microservices spread out across repos, you've got a challenging context engineering problem. Read this:

Daniel Hensley @dw_hensley

2 months ago

Highly distributed, multi-codebase systems have structural properties that current AI coding tools can't navigate. What we've found is that the multi-codebase problem is a context problem, and context problems are solvable if you treat them as compilation rather than search.

1 2 2 363 1

0 0 1 56 0

View Details

Joe Schmid @jschmid

2 months ago

Yes, we’re seeing this at @driver_ai_ with a wide variety of experiences at our customers adopting AI coding agents. You want to be careful with context engineering *and* harness engineering. Driver provides dynamic context engineering and we’re soon going to release the results our own internal harness engineering work.

Santiago @svpino

2 months ago

Obviously, models are a big deal, but coding harnesses play a huge role in making these models look good. I suspect that you can get the best frontier model out there, put it in a shitty harness, and the experience will be very disappointing. The reverse is also true: put a

37 7 79 12K 14

0 0 0 38 0

View Details

Joe Schmid @jschmid

4 months ago

@chrisalbon Claude Chic + RoboRev

0 0 0 66 0

View Details

Ben Williams @biwills

5 months ago

git worktree is going to be everywhere in 2026

0 1 2 153 1

View Details

Joe Schmid @jschmid

4 months ago

A thoughtful take on emerging AI development. This resonates with me.

Matthew Rocklin @mrocklin

4 months ago

Some thoughts after having spoken to a bunch of people exploring AI development matthewrocklin.com/ai-beginners/

0 0 4 524 4

0 0 1 32 0

View Details

Joe Schmid @jschmid

4 months ago

@mrocklin This is so cool! Check out @conductor_build too for a GUI version that manages multiple agents working in parallel.

1 0 1 123 0

View Details

Joe Schmid @jschmid

5 months ago

Another innovation from @PrefectIO. Horizon is the control plane for managing MCP at scale.

Jeremiah Lowin @jlowin

5 months ago

21 34 494 287K 597

0 0 1 58 0

View Details

Jeremiah Lowin @jlowin

5 months ago

Every company will have a context layer. Today, we're launching Prefect Horizon to make that possible. The context layer is where AI agents interface with your business. It's where teams expose their proprietary data, tools, and workflows to autonomous systems. It’s where you define what agents can see and do, and where you curate and distribute the context that makes automation useful and safe. We know what this requires because we created FastMCP, the standard framework for working with the Model Context Protocol. We've watched it grow to a million downloads a day. But we've also seen the protocol's limits. MCP describes how to *build* a server, not how to govern it at scale. It doesn't tell you how to deploy it, secure it, or share it with 5,000 colleagues. We built Prefect Horizon to turn MCP from a protocol into a platform: 🚀 Horizon Deploy: Managed MCP hosting that gets you from PR to governed endpoint in 60 seconds. 📚 Horizon Registry: A central catalog for all your MCP servers, including first-party, third-party, and our new remix servers that compose multiple sources together. 🔐 Horizon Gateway: The control plane enterprises need: role-based access control down to the tool level, audit logs, and usage visibility. 💬 Horizon Agents: The last mile. Give business users an agentic interface to your company without ever knowing what "MCP" is. This is automation for the context era. Welcome to Horizon.

21 34 494 287K 597

View Details

Joe Schmid @jschmid

5 months ago

@SahilBloom “The Supreme Personality of Godhead said: The giving up of activities that are based on material desire is what great learned men call the renounced order of life [sannyāsa]. And giving up the results of all activities is what the wise call renunciation [tyāga].”