coderfpv

you can just build things

@richsongocrazy Try vscode with copilot or even opencode using latest models from anthropic, openai and kimi moonshot

This is trash guys.

Kenya has breathtaking landscapes.

💫 New LangChain Academy Course: Building Reliable Agents 💫 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️ academy.langchain.com/courses/buildi…

pytorch is over. it's done. no one is using it anymore. keep up

@SiroFitsKe 140km already is crazy numbers. You got good endurance. Safe travels

Past Emali, 140km done, 200km to Moshi, 100 km to Oloitokitok. Hali ya anga ni shwari kabisaa

I read somewhere that knowing so much is a curse… and it’s so true.

Current AI is a librarian of existing knowledge. Science requires an explorer of the unknown. You don't win a Nobel Prize by staying in the library.

Meet the new Stitch, your vibe design partner. Here are 5 major upgrades to help you create, iterate and collaborate: 🎨 AI-Native Canvas 🧠 Smarter Design Agent 🎙️ Voice ⚡️ Instant Prototypes 📐 Design Systems and DESIGN.md Rolling out now. Details and product walkthrough video in 🧵

Nobody prepares you how hard it is to sell your product. Building seems to be the easy part now, selling is the new hard now.

@svpino We are entering an era of slop open source

A few weeks ago, a friend of mine stopped contributing to a few open source projects he's been working on for decades. He just stopped. "I don't want to train my replacement for free," were his exact words. I keep thinking about this. I really don't know what will happen to open-source projects in the next few years, when it becomes painfully obvious that developers no longer care because they can just "build" whatever they need on the spot. By the way, I don't think developers should be reinventing the wheel every time, and there's absolutely zero chance that leads to better software, but the reality is that many people don't care anymore.

@axmedalxender i dont know anybody but they should try this product. noverdesk.com

The average Kenyan Techie: >Drives a slightly used German car (usually a BMW, Audi, or Mercedes) >Still flexes about being a strong Java developer >Hates on the greenhouse that are actually doing good stuff >Opens every website and immediately clicks Inspect Element >Spends more time looking for bugs in other people’s work than building his own >Constantly criticizes other developers’ projects and startups > They will come under this post and tell me to shut up and show us what you did >Publicly hates AI and vibe coding >Secretly uses all AI tools late at night >Frequently spotted at Java House meetings >Weekend drives to Limuru while discussing “the tech ecosystem” >Writes long threads explaining why other people’s projects won’t scale >Says things like “this architecture is wrong” after looking at a website for 10 seconds >Has at least 3 unfinished side projects on GitHub

“Attention Residuals” is now available on AlphaXiv! In standard transformer, every layer just inherits an equal sum of all earlier layers, so as models get deeper, useful computations get diluted instead of being selectively reused. The research team at @Kimi_Moonshot proposes Attention Residuals which fixes this by letting each layer attend over previous layers with learned weights, so depth works more like retrieval than accumulation. This makes training more stable, improving scaling and downstream performance with almost no extra overhead, specifically, 1.25x compute advantage against the standard transformer baseline.

😂😂

Introducing 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with learned, input-dependent attention over preceding layers. 🔹 Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth. 🔹 Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale. 🔹 Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead. 🔹 Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains. 🔗Full report: github.com/MoonshotAI/Att…

🔥 Meet Mistral Small 4: One model to do it all. ⚡ 128 experts, 119B total parameters, 256k context window ⚡ Configurable Reasoning ⚡ Apache 2.0 ⚡ 40% faster, 3x more throughput Our first model to unify the capabilities of our flagship models into a single, versatile model.

“Action produces information. Just keep doing stuff.” - Brian Armstrong, CEO Coinbase #ZFellows #PurposeDriven #BuildSomethingThatMatters #EntrepreneurMindset #MeaningfulWork

I just watched Jensen walk on stage to the deployment of @Starcloud_-1 😱🤯😲 @NVIDIA GTC

Announcing NVIDIA DLSS 5, an AI-powered breakthrough in visual fidelity for games, coming this fall. DLSS 5 infuses pixels with photorealistic lighting and materials, bridging the gap between rendering and reality. Learn More → nvidia.com/en-us/geforce/…

@OmriBuilds Customer Service Agents noverdesk.com

Pitch your startup in 3 words

1 million context window: Now generally available for Claude Opus 4.6 and Claude Sonnet 4.6.