Syam Anupom

Building ❤️+ 🤖

[Aaron Levie @levie]

Coding is basically the pinnacle of what you could reasonably automate with AI, and yet we still need human engineers to oversee agents for them to be effective.

The AI models are trained on an incredible amount of sophisticated code. The users are highly technical and can use the latest tools quickly. The work is “verifiable” because you can test an app. The outcomes are often removed from the quality of the code (you can have sloppy code but the app can still work). And the context for the agent is often already digitized and sitting in the codebase.

That’s an incredible amount of benefits that AI coding agents get to work with. Some of those apply to knowledge work, but most don’t in areas where the work needs to be fully reviewed to be useful, or where data isn’t as abundantly digitized. This makes the job for agents in knowledge work more complicated.

So if with all of that, engineers still remain in very high demand, the risks are going to be less than what’s perceived for other areas of knowledge work. Agents will let people do far more than they did before, but the people don’t go away.

[Syam Anupom @syamantics]

@hthieblot Self-improving

[Syam Anupom @syamantics]

If AI can automate a lot of software engineering then it should be able to automate most work people do on computers too.

But the reason engineers are getting so much value from AI is not only because AI is good at code. It is also because engineers know how to work with it.

Most other users don’t work like software engineers today. A finance person may understand invoices, policies, approvals, vendors, all of that. They may also be able to build a rudimentary working system around the AI. But they don’t have the right tools, know-how’s and mindset to continuously monitor, maintain and improve it.

That gap is why forward-deployed engineers are becoming so important. They are basically going into finance, ops, legal, support, sales etc and building the scaffolding that makes AI useful there.

Current pattern is: AI becomes more useful when someone builds the right system around it.

But what if we can get this process productized? What if we give regular users the same kind of controls (albeit at much higher level) that engineers have with AI? Or even better what if AI systems are by default SELF-HEALING and SELF-IMPROVING?

[Syam Anupom @syamantics]

@kenwuuuu Claude Agent SDK

[Syam Anupom @syamantics]

If agents could somehow talk to each other, we would need way less human involvement. It is fairly easy for them to interact autonomously. But really hard to do this in a meaningful and reliable way. Making it work requires some engineering. So to remove humans from the loop, we would first need humans to build the loop first (and possibly to maintain the loop as well).

[Syam Anupom @syamantics]

@dharmesh From a developer POV:

Skills > Harness > Model

[Syam Anupom @syamantics]

Exactly!

[dharmesh @dharmesh]

The harness matters more than the model.

Models have gotten really good. Great reasoning, large context windows, better instruction following.

But, what makes *use* of those capabilities is actually the harness. It's what provides tools, memory, skills and context to the model.

ChatGPT is a harness. Claude Cowork is a harness.

Without the harness, the model is just an engine with no car. You don't get anywhere.

[Syam Anupom @syamantics]

I guess they are trying to do what YC is calling self improving company - not sure how it’ll work at Meta scale.

[Syam Anupom @syamantics]

Had a deep chat over the long weekend with 2 not laid-off meta employees.

The company feels broken from inside out, no one is motivated to do any work. While everyone’s expected to pull projects with 1/3rd resource. Orgs got flattened with some Sr. Directors having 70+ direct reports. On top of that they are having AI to record keystrokes and browser. Meta is fully bought into the idea of mining employee data to clone them with AI.

Either Zuck and Alex Wang will kill the company or somehow make it 10x stronger.

[Syam Anupom @syamantics]

@DanielSmidstrup Fundraising

[Syam Anupom @syamantics]

@DanielSmidstrup Retention

[Syam Anupom @syamantics]

I have a hypothesis:

Every cycle starts with some fundamental platform shift. And lot of the early innovations are driven by pure research by PhDs. Research at this stage yields quicker RoI.

Then come the builders taking advantage of the underlying technology and solving more surface level problems. Startups find new ways to solve existing problems with the new platform. The demand for makers and engineers shoot up. On the other hand research slowly takes back seat as it goes back to its 10 year long RoI cycle.

And at the end of the cycle you see MBAs coming in. Optimizing across product and GTM, adding revenue with hundreds of incremental improvements from thousands of experiments. Market at this stage is more matured and stable with a high demand for PMs and GTM operators who can consistently drive results.

We are now at the early “second” stage of the cycle. If you’re a builder it’s your time now.

[Syam Anupom @syamantics]

Skills > Harness > Model

Software side optimization is starting to add more leverage. Cycles have always been like this.

[Syam Anupom @syamantics]

One of the best ideas I picked up at Netflix was the full-cycle developer.

A full-cycle developer owns the whole software lifecycle: design, development, testing, deployment, operations, and ongoing support.

With AI, I think we’ll see more of this. Developers will own more of the cycle, sometimes starting as early as product conception and UI/UX.

There will still be a spectrum. Some developers will lean more ops and systems heavy. Others will lean more product and user heavy. But the center of gravity will move toward developers who can take an idea further, faster and with more scope & ownership.

[Syam Anupom @syamantics]

@3dgrinder This is a good one

[Syam Anupom @syamantics]

What is code?
- Code is instructions for the computer.

What is prompt?
- Prompt is instructions for LLM.

How do we write code?
- Python, JS, TS etc.

How do we write prompt?
- Markdown!

[Syam Anupom @syamantics]

Thin controller, fat models.
Thin harness, fat skills.

Don’t overbuild the runtime. Thank me later.

[Syam Anupom @syamantics]

I’ve heard this from at least a dozen YC founders over the last few weeks:

Anthropic may be winning the room with execs while losing the floor with developers.

Their "coding is largely solved" line is great buyer messaging.

Execs hear: engineering productivity is about to change, and Claude is the platform to bet on.

But devs know their job is far from solved. They use Claude because it works.

As Claude reliability slips and OpenAI closes the gap, developer trust in Anthropic is starting to feel fragile.

[Syam Anupom @syamantics]

@euboid @Railway The core issue is blast radius. AI coding tools have no concept of it. A human who's been on the team 6 months knows which deploys need extra review, which configs are load-bearing. Until deployment risk is encoded in the tooling, this keeps happening.

[Syam Anupom @syamantics]

@swyx review bot that catches 60% of issues but gives teams false confidence about the other 40% can be net negative