Building startups. Co-Founder at @GlintsSingapore. Angel investor at @usemitohealth (YC S24), @Munify_fintech, GetASAP (YC S25). Available for consulting.
I asked Claude to compile the Linux kernel from my phone
Seeing a lot of Claude ร Telegram features/bots lately - I've been building one too, but went deep on telegram-native features and focused on development use cases
The fun part is the mini apps:
- Swipe-based code review (tinder for diffs basically)
- Markdown viewer with inline commenting; feedback goes back to Claude
- Terminal viewer for background tasks
Also:
- Forum topics = parallel Claudes
- Voice notes auto-transcribed locally (moonshine)
- Claude can ask follow-up questions mid-conversation (AskUserQuestion)
- Full set of permission prompts mirroring Claude Code - diffs, command previews, approve/deny/accept-all
https://t.co/uI0VpEqc3L
Sandboxing. Sandboxing.
LLMs are unpredictable. With the most advanced models, the chances of such commands being executed is small, but never zero.
You need infrastructure-level isolation.
I'm so angry... the OpenAI team is looking into it, but this feels like something that should happen with GPT-3.5.
Not a mid-2026 frontier model on the highest reasoning level.
As a founder, your number one job is to eat shit.
You're on the call with the upset customer. You need to fire people by telling them directly. You're listening to the engineer who wants to quit due to burnout. You have to read the 40-page vendor contract, which is a nightmare to go through.
Every hard, uncomfortable thing lands on your desk. That's the whole job. You're here to absorb the bad stuff so everyone else can keep moving.
โdonโt train your own modelโ is common ai advice. it's wrong. your token bill's the proof.
today, weโre excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data.
open-weights models are performant and much cheaper. when trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise.
with castform, model training is as simple as prompt engineering. @castformai
bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns.
see what you can build with castform๐
Starting June 15, paid Claude plans can claim a dedicated monthly credit for programmatic usage.
The credit covers usage of:
- Claude Agent SDK
- claude -p
- Claude Code GitHub Actions
- Third-party apps built on the Agent SDK
@noahzweben My goal is just to have a proper, functioning Claude Code experience on mobile
Running on Claude Code on mobile SSH is actually a better experience than the mobile app. I will switch to Codex from June 15 onwards, not worth putting up with this.
@noahzweben I use Claude Code interactively using a Telegram bridge I built via Agent SDK.
Why did I do that instead of the Claude app? Because the app is terrible! It takes forever to connect, I cannot spawn sessions from existing projects, there's no proper VM-based sandboxing...
@moritzkremb https://t.co/pDFNqnMMr1
I have an open-source project that aims to make the best use of Telegram features, including mini apps! So you can truly build on the go
I asked Claude to compile the Linux kernel from my phone
Seeing a lot of Claude ร Telegram features/bots lately - I've been building one too, but went deep on telegram-native features and focused on development use cases
The fun part is the mini apps:
- Swipe-based code review (tinder for diffs basically)
- Markdown viewer with inline commenting; feedback goes back to Claude
- Terminal viewer for background tasks
Also:
- Forum topics = parallel Claudes
- Voice notes auto-transcribed locally (moonshine)
- Claude can ask follow-up questions mid-conversation (AskUserQuestion)
- Full set of permission prompts mirroring Claude Code - diffs, command previews, approve/deny/accept-all
https://t.co/uI0VpEqc3L
Opus 4.6 just shipped with Agent Teams so I did what any sane engineering leader would do.
I asked it to build a global payroll system. For 180 countries.
It spawned 180 teammates. One per country.
4700 tasks on the shared board. 2000+ files across 120 modules. Tax tables for jurisdictions I've never heard of.
The compliance agent and the tax engine agent got into an actual argument about Indonesian PPh 21 withholding brackets. Back and forth. Five messages. The devil's advocate agent sided with compliance.
None of it worked.
But boy was it beautiful.
AI products can't afford to let users' first experience be from a model that's not the absolute best. Many people don't have experience with strong AI products yet, and a mediocre first impression becomes their mental model for the entire technology.
Not sure if it's intentional on Anthropic's part, but defaulting to Opus in Nov 2025 was a pretty smart move.
One reason to be skeptical of AI, and thus adoption, is the first touch experience. Seen this play out - "GPT-4o generates slop output, therefore *all* AI still sucks"
Introducing Claude Sonnet 4.5โthe best coding model in the world.
It's the strongest model for building complex agents. It's the best model at using computers. And it shows substantial gains on tests of reasoning and math.