Rupesh Harode @techytoes

I asked Claude Code to make a new GTA: San Andreas mission where CJ has to vibe code a startup and then raise money from investors in SF

हिंदुओं एक बात कहूँगा बुरा ना मानना। तुमने खुद इस देश के सूअर नेताओं(राजनीतिक दलों) को मजाक बनाने और भौंडी राजनीति सेकने के लिए अपना धर्म किराए पर दे दिया है। थू है ऐसी रामलीला कमेटी पर जो चंद पैसों के लिए धार्मिक इवेंट्स का मखौल उड़ाते हैं।

Rahul Shivshankar @RShivshankar

a week ago

Delhi Ram Leela Organisation Committee will burn a 30 foot Cockroach effigy to mark Dussehra celebrations.

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We are currently living through a history lesson for future Indians on why religion and politics should never be mixed.

Rahul Shivshankar @RShivshankar

a week ago

Delhi Ram Leela Organisation Committee will burn a 30 foot Cockroach effigy to mark Dussehra celebrations.

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@F71uday x.com/narendramodi/s…

Narendra Modi @narendramodi

2 weeks ago

Remembering Loknayak Jayaprakash Narayan on his birth anniversary. His appeal transcended generations. He inspired people to believe that public life could be an instrument of national renewal. His fearless voice during the Emergency was also noteworthy. Loknayak JP also was

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@F71uday @narendramodi @ECISVEEP Mahamanav ko ye sab se farak nahi padta

Hi @DalotDiogo I’ve got a bit of a heavy hangover today and I might die If I do, do you fancy lifting the coffin at the burial, so you can let me down one last time? Thanks mate x

dalot bhi

Pranav @pranav_fc

a month ago

Tukaram mundhe sir Marcus Rashford naam ka ek darinda bacho ko ganda khana khilata hai. Sir please arrest him

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80% of personal websites only have a single blog post from two years ago where the author mentions they want to write more

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system. External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent. With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both! I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering). [Original text: The Batch]

As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes: 1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship 2. Builder: quickly turns a prototype/idea into production-grade product/infra 3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance 4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit 5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS. A healthy team needs a mix of these, depending on the product: - A product that is new and pre-PMF needs people that are strong at 1+2+3 - A product that is growing and has found PMF needs 2+3+4 and some 5 - A product that has strong PMF needs 3+4+5 and some 2 Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?

Apply critical thinking and continue to learn the fundamentals so we don’t turn into token munching maniacs.

Trusting any single AI vendor seems like an increasingly high risk for any team or company. When using models: use it behind a router where it's trivial to switch providers as soon as one tries to force unacceptable T&Cs like Anthropic with Fable. When using harnesses: do the same. Use ones where models are trivial to switch out, like OpenCode, Factory, Cursor and many others. Putting all your dependencies on one provider increasingly feels like a massive business risk that makes little to no sense to take. Unless you have a hobby project, of course. Then convenience is all that matters. But if you're a professional, make it dead simple to offramp from one provider to the other!

how to be good at your job - realize this one thing is actually made up of two separate things - realize instead of solving the direct problem you can solve a broader problem - instead of implementing thing, implement other thing that makes it easier to implement thing

My concern for the AI era, or at least this phase of it, is that a generation is being taught that "close enough" is just fine. Take @AnthropicAI for example. Text wrapping in Claude Code has been broken for weeks. Superfluous spaces appear on the left edge. One engineer to another: you know its an out by one error. I refused to believe that nobody has noticed this. The shtick they are selling is that AI can fix this kind of thing. Either they tried to prompt a fix, and Claude ain't good enough to fix an out-by-one error. Or they haven't attempted it because it is "close enough". It can't be the case that AI is only good enough if we lower our standards. It can't. I'm well aware I have both feet firmly planted in my "grumpy old man" phase of life...

First, the cloud gave us abstractions that made it easier to forget failure. Infrastructure became someone else's problem, and many systems were designed around happy paths with reliability added later ... managed services remain up ... Now, it is an AI speed-at-all-costs culture that is forcing us to ship code that just works 80% of the time, because it is okay if things fail 20% of the time, but we need to ship faster. Software was supposed to be predictable. Somewhere along the way, we started treating reliability as a tradeoff, and it is now becoming the norm. I do hope we come back to building software that people can depend on and something we are proud of.

The 10x engineer was always mythical. The 100x engineer — one senior + the agents they direct — is real. I watched it work this year. Same pattern runs through every role. Which half of your job is translation, and which half is judgement? ajeygore.in/content/ai-ate…

This is the the quote I've been citing a lot recently.

kache @yacineMTB

9 months ago

you can outsource your thinking but you cannot outsource your understanding

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Sunday afternoon + Codex = an absurd amount of progress

For a small team like @zarityhealth that’s building custom tools from the scratch, @claudeai @antigravity @cursor_ai @stitchbygoogle..have levelled the field.Our weakness became our strength. Deleted 2 saas tools in the past 2 months. Excited for what we can own end to end next.

@DexxterUtd you started with the agenda already