My timeline needs more people who build things. 👀 developers, designers, AI people, indie hackers, founders, or anyone experimenting with cool ideas. let’s connect, share what we’re working on, and learn from each other 🤝

Using Claude Code in the terminal is mostly an ego thing. The desktop app looks better and does the job. Prove me wrong. What does the CLI do that the app can't?

The 15 stages of every vibe coder's backend: 1. "Just use localStorage, it's fine" 2. "Okay, let's add a real database" 3. Picks Supabase because of a tweet 4. Forgets to turn on row level security 5. API keys pushed to a public GitHub repo 6. "Why is my OpenAI bill $847?" 7. Asks the AI to "make it secure" 8. It rewrites the entire backend 9. Nothing works anymore 10. Reverts to 3 days ago 11. Adds auth. Breaks login for everyone 12. "Who needs migrations?" 13. Production database deleted by an agent 14. Learns what a backup is 15. Hires a backend engineer Which stage are you on?

Guys, I don’t understand. Chinese models are clearly very strong, especially at coding like for example, DeepSeek is very good at coding So why does everyone spend $200 on Claude or in on ChatGPT subscriptions? 🤔

The MOST obvious (but REALLY good) STARTUP ideas right now: 1. Zestimate for company data (what your data is worth to an AI lab) 2. Google Analytics for agents (see which agents visit your site and what they want) 3. Y Combinator for 1 person companies (back solo founders running a team of agents) 4. Okta for agents (who each agent is and what it's allowed to touch) 5. Marie Kondo for your company's data (clean it up so agents can use it) 6. FICO for agents (a trust score before an agent can spend money) 7. Geico for agents (insurance for when an agent makes an expensive mistake) 8. Datadog for agents (see every step your agents took) 9. Twilio for agents (phone numbers and texting for agents) 10. Upwork for agents (hire an agent for a job instead of a freelancer) 11. ESPN for AI (every model launch covered like a big game). 12. Shutterstock for robot training videos (get paid to film yourself doing chores) 13. Credit Karma for what AI knows about you (see it, fix it, delete it) 14. Norton for AI scams (protect your parents from fake voices and emails) 15. PayPal Buyer Protection for agent purchases (fixes it when your agent buys the wrong thing) 16. Geek Squad for AI (set up personal agents for families and small businesses) 17. Dell for private AI (AI in a box for firms that won't use the cloud) 18. Carfax for used GPUs (a health report before you buy secondhand) 19. Airbnb for idle GPUs (rent out computing power you're not using) 20. Rotten Tomatoes for AI models (real reviews instead of benchmarks) 21. G2 for skills (find the best skills for your agent) 22. Dropbox for your AI memory (take what your agent knows about you to any agent) 23. Canva for physical products (design it with AI and get it made) 24. ADT for your personal agent (alerts when your agent does something it shouldn't) 25. Hertz for robots (rent one for a move, a deep clean or an event) 26. Harvard Business School for internet businesses (a real education in building online) 27. Workday for agents (onboard, manage and offboard your digital workers) 28. Getty Images for your face and voice (license your likeness to AI safely) 29. Meta Muse for older adults (and other high LTV niche) 30. Google Ads for agents (pay to be what the agent recommends) 31. Loom for agents (a walkthrough of everything your agent did) 32. Plaid for agents (approved access to banks, airlines and stores) 33. Jiffy Lube for vibe coded apps (quick fixes when your AI built app breaks) 34. Ancestry for datasets (where your training data came from) 35. The organic label for human-made content (proof a person made it) 36. Strava for builders (track what you ship every week) 37. Honey that haggles for you (your agent negotiates the price) 38. SEM Rush for personal agent platforms (like Muse etc) 39. Frequent flyer miles for agents (loyalty programs built for agents)

Hey @X, Looking to connect with people interested in: 🎮 Mobile game development 🤖 AI agents ⚡ Vibe coding 🛠️ Building in public 🎨 Pixel art and game design 📱 Indie apps and side projects 🎬 Dev YouTube Building my first mobile game right now. What are you building? Say hi 👋

Create these 6 files before you start vibe coding: • PRD.md: what you're building and for whom • ARCHITECTURE.md: stack, folders, data flow • DESIGN.md: colors, fonts, components, vibe • RULES.md: coding style and what the AI must never do • TASKS.md: small steps, one at a time • PROGRESS.md: what's done, what broke, what's next No files = the AI guesses. 6 files = the AI follows your plan. 10 minutes of writing saves 10 hours of fixing. Bookmark this before your next build.

Founders and builders, let’s connect 🤝 🧠 AI & tech 💻 SaaS & products 🚀 Startups 🛠️ Building in public Currently building Pingara 📡 Always keen to connect with like-minded people, share ideas, and see what everyone’s working on. What are you building? 👇

Okay, let me join the trend 👋🏼 I’m looking to connect with awesome people who are actively building and shipping cool stuff. If you are a - founder - SaaS builder - indie hacker - developer - vibe coder Drop a comment, show me what you work on, and let’s connect! 👇🏼

If you learn AI Research Engineering, you will never be unemployed again. Stage 1: Tensor Math & Custom Autograd - Learn: PyTorch internals, custom forward/backward passes, Triton programming, memory layouts and stride calculations. - Practice: Write a custom fused attention kernel in Triton and a custom backward pass for a novel activation function without using PyTorch’s `autograd`. - Why: You cannot optimize or debug a collapsing loss curve if you treat neural networks as black-box APIs. Stage 2: Scaling Laws & Data Mixing - Learn: Chinchilla optimal compute, data deduplication (MinHash), domain-weighting and learning rate scheduling (Cosine with warm restarts). - Practice: Train a 100M parameter model on 3 different data-mix ratios (code vs. math vs. web text) and plot the exact loss divergence curves. - Why: In 2026, model architecture is secondary. The secret sauce is the exact mathematical ratio of the training data. Stage 3: Next-Gen Architectures (Beyond Transformers) - Learn: Mixture of Experts (MoE) routing, State-Space Models (Mamba/S4), linear attention and Ring Attention for infinite context. - Practice: Implement a sparse MoE routing layer with a load-balancing loss from scratch and benchmark its throughput against a dense transformer. - Why: The quadratic bottleneck of standard attention is being actively replaced. You must know the physics of the alternatives. Stage 4: Synthetic Data & Curation Pipelines - Learn: Self-instruct, evolutionary prompting, rejection sampling, and using frontier models to generate and filter training data. - Practice: Build a pipeline that uses a heavy reasoning model to generate 50,000 complex logic traces, filters them via a verifier and trains a smaller model on the survivors. - Why: The internet is running out of high-quality human text. The ability to mathematically curate synthetic data is the ultimate moat. Stage 5: Advanced Alignment & Preference Optimization - Learn: DPO (Direct Preference Optimization), KTO (Kahneman-Tversky Optimization), ORPO, reward modeling and mitigating reward hacking. - Practice: Align an SFT model using KTO (which only requires binary "good/bad" signals not paired preference data) and measure the shift in its behavioral distribution. - Why: SFT teaches a model what to say. Alignment teaches it how to behave without requiring millions of dollars in human annotation. Stage 6: Distributed Training & Systems ML - Learn: 3D Parallelism (Tensor, Pipeline, Data), DeepSpeed ZeRO-3, FSDP, NCCL communication primitives and gradient checkpointing. - Practice: Shard a 7B parameter model across 4 GPUs, profile the NCCL communication overhead and optimize the tensor-parallel boundaries. - Why: Single-GPU training is for tutorials. Production research requires sharding weights and optimizer states across clusters without bottlenecking the interconnect. Stage 7: Multimodal Fusion & Projection - Learn: Vision encoders (SigLIP, CLIP), cross-attention mechanisms, audio tokenization and early vs. late fusion architectures. - Practice: Train a lightweight MLP projection layer that maps SigLIP vision embeddings into an LLM's text embedding space to create a custom Vision-Language Model. - Why: Text is only half the internet. The 2026 meta is building models that natively reason over vision, audio and text simultaneously. Stage 8: Inference-Aware Model Design - Learn: Grouped Query Attention (GQA), Multi-Query Attention (MQA), KV-cache quantization and speculative decoding compatibility. - Practice: Modify a standard transformer to use GQA, measure the exact drop in KV-cache VRAM usage and benchmark the latency gain in vLLM. - Why: An MLE who trains a model that is too slow or expensive to serve has built a useless model. Architecture dictates inference physics. Bookmark and Repost!