Fully Remote Dev

everything is computer.

[Fully Remote Dev @FullyRemoteDev]

@theRCKR @davidasinclair Mobile phones were once only for the elite and powerful.

[Fully Remote Dev @FullyRemoteDev]

@nilayparikh yeah saw that.

[nader dabit @dabit3]

One of my only gripes with OpenClaw is that it’s kind of slow.

Today I’m going to be trying out ZeroClaw, OpenClaw rebuilt in Rust and optimized for speed. Seems insanely fast.

I don’t know much about the creators but one of them is @argenistherose, he’s at Harvard studying CompSci

Quick benchmark vs OpenClaw
• Binary: 3.4MB vs 28MB
• Cold start: 0.38s vs 3.31s
• Status cmd: ~0s vs 5.98s
• RAM (status): 7.8MB vs 1.52GB

https://t.co/UtKd4peXkW

[Andrej Karpathy @karpathy]

I think it must be a very interesting time to be in programming languages and formal methods because LLMs change the whole constraints landscape of software completely. Hints of this can already be seen, e.g. in the rising momentum behind porting C to Rust or the growing interest in upgrading legacy code bases in COBOL or etc. In particular, LLMs are *especially* good at translation compared to de-novo generation because 1) the original code base acts as a kind of highly detailed prompt, and 2) as a reference to write concrete tests with respect to. That said, even Rust is nowhere near optimal for LLMs as a target language. What kind of language is optimal? What concessions (if any) are still carved out for humans? Incredibly interesting new questions and opportunities. It feels likely that we'll end up re-writing large fractions of all software ever written many times over.

[Fully Remote Dev @FullyRemoteDev]

@aditiitwt

[kepano @kepano]

1. install Obsidian 1.12
2. enable CLI
3. now OpenClaw, OpenCode, Claude Code, Codex, or any other agent can use Obsidian

[CodeRabbit @coderabbitai]

Introducing CodeRabbit Issue Planner! ✨

AI agents made coding fast but planning messy.

Turn planning into a shared artifact in your issue tracker, grounded in related issues and decisions.

Review prompts as a team, then hand them off to an agent!

[Fully Remote Dev @FullyRemoteDev]

@pcshipp porkbun

[Developers @XDevelopers]

Officially launching X API Pay-Per-Use

The core of X developers are indie builders, early stage products, startups, and hobbyists

It’s time to open up our X API ecosystem and instill a new wave of next generation X apps

We’re so back. https://t.co/FS66GuIYJD

[Virat Singh @virattt]

Dexter hit 10,000 stars on GitHub.

It’s OpenClaw and Claude Code, for finance.

What Dexter can do:
• find undervalued stocks
• break down financials
• turn research into a thesis

All our code is open source.

[OpenClaw🦞 @openclaw]

🦞 OpenClaw v2026.2.6 is here!

🧠 Opus 4.6 + GPT-5.3-Codex support
⚡ xAI Grok + Baidu Qianfan providers
📊 Token usage dashboard
🧭 Voyage AI for memory
🔒 Skill code safety scanner
🔧 Cron fixes galore

Security hardening across the board 🛡️ https://t.co/2XU4FHZrBT

[Andrej Karpathy @karpathy]

A lot of people quote tweeted this as 1 year anniversary of vibe coding. Some retrospective -

I've had a Twitter account for 17 years now (omg) and I still can't predict my tweet engagement basically at all. This was a shower of thoughts throwaway tweet that I just fired off without thinking but somehow it minted a fitting name at the right moment for something that a lot of people were feeling at the same time, so here we are: vibe coding is now mentioned on my Wikipedia as a major memetic "contribution" and even its article is longer. lol

The one thing I'd add is that at the time, LLM capability was low enough that you'd mostly use vibe coding for fun throwaway projects, demos and explorations. It was good fun and it almost worked. Today (1 year later), programming via LLM agents is increasingly becoming a default workflow for professionals, except with more oversight and scrutiny. The goal is to claim the leverage from the use of agents but without any compromise on the quality of the software. Many people have tried to come up with a better name for this to differentiate it from vibe coding, personally my current favorite "agentic engineering":

- "agentic" because the new default is that you are not writing the code directly 99% of the time, you are orchestrating agents who do and acting as oversight.
- "engineering" to emphasize that there is an art & science and expertise to it. It's something you can learn and become better at, with its own depth of a different kind.

In 2026, we're likely to see continued improvements on both the model layer and the new agent layer. I feel excited about the product of the two and another year of progress.

[Fully Remote Dev @FullyRemoteDev]

@code_rams @openclaw bot name reference win!

[OpenClaw🦞 @openclaw]

🦞 OpenClaw 2026.1.30

🐚 Shell completion
🆓 Kimi K2.5 + Kimi Coding: run your claw for free
🔐 MiniMax OAuth: one more model just a login away
📱 Telegram got a glow-up — 6 fixes from threading to HTML rendering

Plus a bunch of community-contributed fixes across LINE, BlueBubbles, routing, security & OAuth.

The lobster provides 😏 https://t.co/KyWuiuTzos

[Fully Remote Dev @FullyRemoteDev]

@pedonazi @moonsewage @_trish_xD pointers are just variables which can hold only one data type - memory addresses

[Fully Remote Dev @FullyRemoteDev]

@championswimmer @IndraVahan what are the specs? or is it the GTR9 Pro?

[Andrej Karpathy @karpathy]

A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.

Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.

Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.

Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.

Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.

Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.

Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.

Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

[OpenClaw🦞 @openclaw]

🦞 BIG NEWS: We've molted!

Clawdbot → Moltbot
Clawd → Molty

Same lobster soul, new shell. Anthropic asked us to change our name (trademark stuff), and honestly? "Molt" fits perfectly - it's what lobsters do to grow.

New handle: @openclaw
Same mission: AI that actually does things.