Muhammad Farhan

@AbdullahSays99_ HBO Max, few quantity but high quality.

Netflix and Amazon Prime are getting really boring. I can never seem to find anything that's actually worth watching. Can anyone suggest something?

@benjitaylor yeah, devs 'vibe-coded' the health app, so now my task is to make it not look vibe-coded. 😂

The challenge is not vibe coding, the challenge is making it not look vibed

@shoaib100mph Aura 👌

I never listened to this, till date

@jullerino Codex supreme 👏

OpenAI have some really good designers working on Codex. This little sheet is just nice, do I just steal it or is there something else to be made here🤔

@GeminiApp training ground.

Great news: Users in India can now upload and edit videos directly with Gemini Omni! Get started in the app or gemini.google.com: > Upload your video > Tell Gemini the change you want to make > Enjoy your new creation We can't wait to see what you make!

@charles_lukes I use both claude and antigravity. 😂

senior devs: codex, claude mid dev: cursor junior dev: antigravity

@badlogicgames it's ai generated...

if we see this, we know

@ManCity great coach, even greater human being...

Pep Guardiola to step down after incredible decade as City Manager 🩵 🔗 mancity.co/Pep-Guardiola

@designcoursecom @threejs nice

What's strange to me is that there's a bunch of really sick visual experiments being achieved with @threejs /tsl, but very few actual games. This is one of the best ones I've seen so far.. Give me more examples!

@kilocode Tasteless

Grok Build 0.1 might be one of the most underestimated AI models right now. We tested it in Kilo Code by asking it to build 5 websites from scratch. Here are the results:

@mattpocockuk /grill-me I want to say congrats to matt

Happy 100K stars day!

@tdinh_me ai agent needs a good context

Claude Code can actually do everything, you just need to know how to ask.

@OfficialLoganK @geminicli Error: Unable to login through my google account.

@geminicli Very excited to unify the experience for developers building with Gemini under Antigravity! Hoping this adds more clarity for those asking what product is right for them.

Transitioning Gemini CLI users to Antigravity CLI We are unifying our efforts around a single harness and platform, Google Antigravity with four distinct surfaces: • Antigravity 2.0 • Antigravity CLI • Antigravity SDK • Antigravity IDE This will allow us to move faster and give you a streamlined experience wherever you do your best work. Rebuilt in Go for speed, Antigravity CLI is available today and brings robust multi-agent orchestration and asynchronous workflows to your terminal. Important things to know: 1. If you are using Gemini CLI through your Google one account (Google AI Pro or AI Ultra) or through Gemini Code Assist for individuals (free offering) we will be helping you migrate your workflows over the next 30 days. 2. No action required for Enterprise users. Enterprise plans and API keys will continue to be supported in Gemini CLI. Read the full details in our blog post → goo.gle/4eWkUgK

@hii_mohit building is easy. marketing is tough.

Any successful indie app founders here? Is this true?

@iishaparekh True that...🫠

marketing is 100x harder than programming

@signulll story telling will always be the differentiator...

spreadsheets don’t raise rounds. decks don’t recruit engineers. roadmaps don’t convert users. but a good story does all three.

@ProductHunt elevate your clinic income

pitch your product in 5 words or less 👇

@thdxr just today purchased opencode go...any tips?

opencode go is currently zero data retention however we can increase limits and make it all more sustainable if we collect data to train future open source models you can opt out of this - is that something you'd be ok with?

@mattpocockuk signed up for you before hype...🤓

@karpathy Beautiful, how you constantly name it 'LLM,' which is the actual word for this technology, instead of scaring people with 'AI, AI...'

Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights: The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons: 1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing. 2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc. 3. LLM knowledge bases as an example of something that was *impossible* with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc. I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3). The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base *and* 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to... Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors.