Jess Martin

Jess Martin

@jessmart.in

🛠 Tools for a better tomorrow 🧠 Architect-philosopher 💼 Building https://lifebuild.me 🏠 https://jessmart.in 📍Fuquay-Varina, NC

1.7K followers376 following730 posts

Coolest thing I’ve read all year

Eugene Vinitsky 🍒@eugenevinitsky.bsky.social

www.percepta.ai/blog/can-llm... As a research lark at Percepta, Christos embedded a computer into an LLM, showed that it could solve the hardest Sudokus, and then as a side bonus built an exponentially faster attention

Overnight on lifebuild.me I asked Codex to: - Swap out my in-product agentic loop for pi.dev - Refactor the entire UI, removing dead code - Create custom project icons in-app w/ Nano Banana Pro - Create a LifeBuild MCP server 📈 models ➡️ 📈 ambition

Something people don't talk about enough when working with AI is just how much *fun* it is. It's not just about productivity! Claude Code is fun. Conductor is fun. ClawdBot is fun. Lean into the joy, folks!

So far, I've written about the Software Factory and the Software Library. Factory has gotten all the play. No one knows what to do with Library (or why they even need one). People saying things like "I run 3-4 agents at a time, life is good! we've arrived!" That's not the factory. Not even close.

We're talking hundreds of simultaneous agents. For that, you need context to maintain alignment. And that takes a Library. I'm not saying we the best solution yet, but I am saying this is going to be a MAJOR problem that more people need to start thinking about.

The easiest path to malleable software: put an agent inside your app that has access to the repo for the product and can implement fixes and push PRs in response to the user pointing out problems. "Self-healing software"?

The Software Factory solves an operational problem: how do we orchestrate agents to deliver high-quality software. Once you know what to build, the factory gets to work. It doesn't solve the alignment problem. How do you keep the factory fed? Let me explain. x.com/jessmartin/...

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And we're building an AI librarian to maintain it. His name is Conan. Conan watches context streams, decomposes into atomic notes, establishes links, assembles slugs on demand. His human boss makes judgment calls. Conan handles volume.

The Software Factory solves an operational problem: how do we orchestrate agents to deliver high-quality software. Once you know what to build, the factory gets to work. It doesn't solve the alignment problem. How do you keep the factory fed? Let me explain. x.com/jessmartin/...

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Give an agent the full context and the micro-decisions improve. Not because the model got smarter—because the context got richer. This is a management problem, not a model problem.

We're calling this a Context Library. There are two related context: - The Library: complete knowledge base covering past, present, future - The Context Constellation: curated context assembled for a specific task

The Software Factory solves an operational problem: how do we orchestrate agents to deliver high-quality software. Once you know what to build, the factory gets to work. It doesn't solve the alignment problem. How do you keep the factory fed? Let me explain. x.com/jessmartin/...

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None of this gets written down. Almost none of it reaches the agent. Agents typically receive: - WHAT: specs, requirements - HOW: codebase, process instructions

What they don't get: - WHERE: What's adjacent? What breaks if you touch it wrong? - WHEN: What did we try before? Why did it fail? - WHY: What objective does this serve? WHERE, WHEN, and WHY are the missing bundle.

The Software Factory solves an operational problem: how do we orchestrate agents to deliver high-quality software. Once you know what to build, the factory gets to work. It doesn't solve the alignment problem. How do you keep the factory fed? Let me explain. x.com/jessmartin/...

There's a growing body of work on why separate AI agents struggle to make aligned decisions. They miss nuance. Break adjacent systems. Repeat past mistakes. The usual diagnosis is the agents aren’t good enough yet. But I think that's wrong.

The real issue is we've been starving agents of context human devs absorb by osmosis: - Hallway conversations about why the last approach failed - The senior engineer who says "we tried that in 2024" - Ambient awareness of what's adjacent, what's fragile

I was wrong. It's not JUST the year of the Software Factory. It's the year of agent orchestration, generally. Across all domains. I wasn't seeing the larger movement. Now I can't un-see it.

I use conductor.build every day and love it. I wonder if they are thinking about higher-level orchestration? I hope so! someone needs to to build it.

conductor.buildConductor - Run a team of coding agents on your MacCreate parallel Codex + Claude Code agents in isolated workspaces. See at a glance what they're working on, then review and merge their changes.
Jess Martin@jessmart.in

Here’s the shift I imagine: tasks flow through a factory line. Imagine software dev as a literal factory. Each stage a station. Tasks enter and flow through, station by station, and emerge as deployed software. Developers don't shepherd. They build and operate the line.

2026 is the year we will stop hand-writing code and fully mechanize software engineering. This might sound like hype. And certainly there’s a lot of hype going around! But something substantial lies underneath the bluster. Here’s how I’m thinking about it.

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2026 is the year we will stop hand-writing code and fully mechanize software engineering. This might sound like hype. And certainly there’s a lot of hype going around! But something substantial lies underneath the bluster. Here’s how I’m thinking about it.

View full thread

2026 is the year we will stop hand-writing code and fully mechanize software engineering. This might sound like hype. And certainly there’s a lot of hype going around! But something substantial lies underneath the bluster. Here’s how I’m thinking about it.

View full thread

2026 is the year we will stop hand-writing code and fully mechanize software engineering. This might sound like hype. And certainly there’s a lot of hype going around! But something substantial lies underneath the bluster. Here’s how I’m thinking about it.

View full thread

We don't know the limits yet. Individual devs using AI are already seeing 5-10x. A factory line? Maybe 100x? The next Google might be 10 people operating a finely-tuned factory.

2026 is the year we will stop hand-writing code and fully mechanize software engineering. This might sound like hype. And certainly there’s a lot of hype going around! But something substantial lies underneath the bluster. Here’s how I’m thinking about it.

View full thread

Here’s the shift I imagine: tasks flow through a factory line. Imagine software dev as a literal factory. Each stage a station. Tasks enter and flow through, station by station, and emerge as deployed software. Developers don't shepherd. They build and operate the line.

Why does this matter? In a factory, improvements compound to the whole org. Optimize one station and every task that flows through benefits. Forever. This is why manufacturing ate craft production. Not skill—system. And hand-crafted software will get eaten the same way.

2026 is the year we will stop hand-writing code and fully mechanize software engineering. This might sound like hype. And certainly there’s a lot of hype going around! But something substantial lies underneath the bluster. Here’s how I’m thinking about it.

For 50 years, developers have shepherded tasks through the software development lifecycle. You pick up a ticket, carry it from planning to implementation to review to deploy. I think that model is ending.

Spending the day working on automating my software factory. Becoming more and more convinced that manufacturing is an appropriate metaphor and mechanism for managing software production.

Just had the wild realization that since I have agents *inside* my product, I can give them a tool to log feature requests + bugs and as they are working with the user. Magic!

Jess Martin reposted

prototyping co-drawing with Gemini Flash 3 at Google in these demos "thinking" is disabled, which makes the model return tokens very quickly (all videos are realtime), and I find these rapid responses pretty good for the use-cases I'm experimenting with, like: executing simple diagrams ...