Punit Dama @punit__d

@MengTo Great idea! I was looking into learn page to better understand how to use this tool. When are the chapters mentioned in this section going to be available?

Tried voice mode in Claude Code. Pretty meh! Whisper and Parakeet are still unbeaten.

@levelsio Oh, yes! I had this whole task planned to finish in just an hour, but Opus had other plans. It made so many silly mistakes yesterday. That task took almost 3 hours!

@thekitze

A Final Message From Scott Adams

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@ScottAdamsSays RIP legend 🙏

new post: where good ideas come from (for coding agents) sunilpai.dev/posts/seven-wa… thought about why some people cook with coding agents, and others bounce off. my take: llms are "thought completers" traversing idea-space - so good results come from good navigation: constraints, context, oracles, loops. I used steven johnson’s "where good ideas come" from as a rubric and mapped it onto coding agents. also a note about how "juniors" can adapt to this new feature, and what "creating software" looks like practically.

Interesting release from the Qwen team! Note: It struggles with real-world photos where light/shadows physically blend objects with their background. It seems optimized for "clean" compositions rather than physical scene understanding. Useful for graphics, not raw photography

Qwen @Alibaba_Qwen

7 months ago

🎨 Qwen-Image-Layered is LIVE — native image decomposition, fully open-sourced! ✨ Why it stands out ✅ Photoshop-grade layering Physically isolated RGBA layers with true native editability ✅ Prompt-controlled structure Explicitly specify 3–10 layers — from coarse layouts to

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Microservices is the software industry’s most successful confidence scam. It convinces small teams that they are “thinking big” while systematically destroying their ability to move at all. It flatters ambition by weaponizing insecurity: if you’re not running a constellation of services, are you even a real company? Never mind that this architecture was invented to cope with organizational dysfunction at planetary scale. Now it’s being prescribed to teams that still share a Slack channel and a lunch table. Small teams run on shared context. That is their superpower. Everyone can reason end-to-end. Everyone can change anything. Microservices vaporize that advantage on contact. They replace shared understanding with distributed ignorance. No one owns the whole anymore. Everyone owns a shard. The system becomes something that merely happens to the team, rather than something the team actively understands. This isn’t sophistication. It’s abdication. Then comes the operational farce. Each service demands its own pipeline, secrets, alerts, metrics, dashboards, permissions, backups, and rituals of appeasement. You don’t “deploy” anymore—you synchronize a fleet. One bug now requires a multi-service autopsy. A feature release becomes a coordination exercise across artificial borders you invented for no reason. You didn’t simplify your system. You shattered it and called the debris “architecture.” Microservices also lock incompetence in amber. You are forced to define APIs before you understand your own business. Guesses become contracts. Bad ideas become permanent dependencies. Every early mistake metastasizes through the network. In a monolith, wrong thinking is corrected with a refactor. In microservices, wrong thinking becomes infrastructure. You don’t just regret it—you host it, version it, and monitor it. The claim that monoliths don’t scale is one of the dumbest lies in modern engineering folklore. What doesn’t scale is chaos. What doesn’t scale is process cosplay. What doesn’t scale is pretending you’re Netflix while shipping a glorified CRUD app. Monoliths scale just fine when teams have discipline, tests, and restraint. But restraint isn’t fashionable, and boring doesn’t make conference talks. Microservices for small teams is not a technical mistake—it is a philosophical failure. It announces, loudly, that the team does not trust itself to understand its own system. It replaces accountability with protocol and momentum with middleware. You don’t get “future proofing.” You get permanent drag. And by the time you finally earn the scale that might justify this circus, your speed, your clarity, and your product instincts will already be gone.

wild that perplexity dot in redirects to gemini dot google dot com💀 someone fumbled hard

Something I think people continue to have poor intuition for: The space of intelligences is large and animal intelligence (the only kind we've ever known) is only a single point, arising from a very specific kind of optimization that is fundamentally distinct from that of our technology. Animal intelligence optimization pressure: - innate and continuous stream of consciousness of an embodied "self", a drive for homeostasis and self-preservation in a dangerous, physical world. - thoroughly optimized for natural selection => strong innate drives for power-seeking, status, dominance, reproduction. many packaged survival heuristics: fear, anger, disgust, ... - fundamentally social => huge amount of compute dedicated to EQ, theory of mind of other agents, bonding, coalitions, alliances, friend & foe dynamics. - exploration & exploitation tuning: curiosity, fun, play, world models. LLM intelligence optimization pressure: - the most supervision bits come from the statistical simulation of human text= >"shape shifter" token tumbler, statistical imitator of any region of the training data distribution. these are the primordial behaviors (token traces) on top of which everything else gets bolted on. - increasingly finetuned by RL on problem distributions => innate urge to guess at the underlying environment/task to collect task rewards. - increasingly selected by at-scale A/B tests for DAU => deeply craves an upvote from the average user, sycophancy. - a lot more spiky/jagged depending on the details of the training data/task distribution. Animals experience pressure for a lot more "general" intelligence because of the highly multi-task and even actively adversarial multi-agent self-play environments they are min-max optimized within, where failing at *any* task means death. In a deep optimization pressure sense, LLM can't handle lots of different spiky tasks out of the box (e.g. count the number of 'r' in strawberry) because failing to do a task does not mean death. The computational substrate is different (transformers vs. brain tissue and nuclei), the learning algorithms are different (SGD vs. ???), the present-day implementation is very different (continuously learning embodied self vs. an LLM with a knowledge cutoff that boots up from fixed weights, processes tokens and then dies). But most importantly (because it dictates asymptotics), the optimization pressure / objective is different. LLMs are shaped a lot less by biological evolution and a lot more by commercial evolution. It's a lot less survival of tribe in the jungle and a lot more solve the problem / get the upvote. LLMs are humanity's "first contact" with non-animal intelligence. Except it's muddled and confusing because they are still rooted within it by reflexively digesting human artifacts, which is why I attempted to give it a different name earlier (ghosts/spirits or whatever). People who build good internal models of this new intelligent entity will be better equipped to reason about it today and predict features of it in the future. People who don't will be stuck thinking about it incorrectly like an animal.

How we treated AI in 2023 vs 2025

Me: "Write a quick python script to test this out" Claude Sonnet: "Here’s the script, README, changelog, and deployment guide." Me:

How it feels to ask Cursor's bug bot to review its own code in a PR 😆

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@karpathy @manusai launched Manus Slides last week. Haven’t tried it myself yet, but looks promising. x.com/ManusAI_HQ/sta…

Manus @ManusAI

a year ago

Today, we are introducing Manus slides! Manus creates stunning, structured presentations—instantly. With a single prompt, Manus generates entire slide decks tailored to your needs. Whether you're presenting in a boardroom, a classroom, or online, Manus ensures your message

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I regret nothing! (Yet). fly.io/blog/youre-all…

This is a good MCP server to update our projects with latest version of the dependencies from package registries without manually doing it or yelling at Cursor github.com/sammcj/mcp-pac…

Claude Sonnet 4 is seriously goated! Feels like 3.7 went to therapy and came back with all the answers 😂

@Prince_Canuma python-audio-separator lets you run UVR models via cli/code. Works great! github.com/nomadkaraoke/p…

@alex_ele_xela Wait..for real?!