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Anything you can do in Obsidian you can do from the command line. Obsidian CLI is now available in 1.12 (early access).

Single biggest improvement to your CLAUDE.md / AGENTS.md: "When I report a bug, don't start by trying to fix it. Instead, start by writing a test that reproduces the bug. Then, have subagents try to fix the bug and prove it with a passing test."

Brilliant! “The Confidence Spiral: The more AI writes, the less you trust your own judgment. The less you trust your judgment, the more you defer to AI. The more you defer, the less you learn. The less you learn, the less you trust yourself. Spiral continues.”

Here's a 1 GB memory reduction for very long Claude Code sessions Before: `() => controller.abort()` Fix: `controller.abort.bind(controller)`

I'm Boris and I created Claude Code. I wanted to quickly share a few tips for using Claude Code, sourced directly from the Claude Code team. The way the team uses Claude is different than how I use it. Remember: there is no one right way to use Claude Code -- everyones' setup is different. You should experiment to see what works for you!

The personal knowledge app we've always dreamed of is here. @openclaw + @fortelabs's PARA method with @tobi's qmd for search Send this to your Claw! x.com/nateliason/sta…

@unclebobmartin Lol you haven't tried @openclaw have you...

constantly having to remind Claude of things we discussed a half an hour ago is frustrating. Piling all these tidbits up in a claude md is frustrating.

claude code + obsidian is infrastructure for agents to think in heres how to get started:

this 2 hour interview with Peter Steinberger (clawd) is a must-watch and i’m not even kidding. he explains his process, how he codes with AI, even advice for new grads. > he ships without checking the code > uses 5-10 agents in parallel > not vibe coding, “agentic engineering” > it’s mentally more exhausting than coding > the people who care less about how things work internally and are excited to build have more success > he has one main project and a few smaller ones running in parallel > makes agent runs tests and iteratively improve base on them > setting up the validation loop and the tests makes reading the code unnecessary > CLOSE THE LOOP: have the agent validate its code and verify the output > don’t just send a prompt with the model. have a conversation with it. spend time getting to the bottom of what you want before handing it off to the agent. > it’s a different way of thinking and building than traditional coding > instead of getting frustrated at the agent for not behaving the way you want, speak with it to understand how it interpreted the task. learn the language of the machine. > you don’t need to plan for days when you can have the agent build and you can check the results in minutes > no CI, if agents pass the test locally he merges > reading the prompt gives you valuable signals just as much as reading the code amazing talk @GergelyOrosz and @steipete 👏🏻

late to the party. I have finally been convinced by multiple awesome developers to give agents another try. this is my first premature contribution: github.com/antfu/skills

had to build an army before I could build @openclaw.

The author of Clean Code is using Claude to write software. I can hardly think of a clearer indicator that coding is now officially outsourced to LLM’s. Who else is mourning the death of coding by hand ?

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.

if you’re getting into clawdbot, know this first: you’re not running an AI. you’re running an economy. tokens = money. clawdbot runs different when you understand the costs. Every message loads your context files ,every browser action costs 10x more than text. crons running every 20 min drain budget silently. the system that actually works: > opus for complex builds, sonnet for daily chat. > switch models mid-session when the task changes. > run local models (ollama + deepseek) for simple stuff, zero cost. > trim your context files (smaller AGENTS. md = less tokens per message) > compact context before you hit the limit, not after. > fewer crons, longer intervals. every 4h not every 20min. burned 50% of my weekly quota in 2 days before figuring this out. Now the same setup runs 3x longer. clawdbot is powerful but it's not free. you gotta treat tokens like a budget and you will outlast everyone.. and ofc consider building an automated economic system for your clawdbot is the move. know the costs, route the models, let the system decide. that's the edge... clawdbot runs on tokens. tokens run on money. once you understand the economy, you're playing a different game..

Your work tools are now interactive in Claude. Draft Slack messages, visualize ideas as Figma diagrams, or build and see Asana timelines.

@karthikponna19 Brave browser vs YouTube app 👎

bro you could have saved $1000 just by using Brave 😭

Btw if you're struggling to get @openclaw running nicely. Just pop the terminal open, type "claude", then say, "help me setup clawdbot - here is a link to the docs - " W

@IndraVahan We just publish an awesome Clawdbot Skills repo. It shows the things you can use in daily life in a more categorized form. Maybe it can be useful for giving an idea. github.com/VoltAgent/awes…