ROBOMARKET
Stay Informed
Daily coverage from 6235 trusted sources — robotics, AI agents, and industry analysis for the GCC.
Last Updated: 06:00 PM (1h ago)
Auto-refresh every 2 hours
OpenAI is building AI agents for everything. Will everyone use them?
Inside the frontier lab’s push to bring AI agents from software engineers to the masses.
Can an LLM Forget the Right Things?
Most LLM inference runtimes have no idea a physical deadline exists. This one refuses admission rather than miss a 33ms robot control cycle, evicts KV cache by meaning instead of age, and is written entirely in hand-written CUDA — no cuBLAS, no libtorch. The post Can an LLM Forget the Right Things? appeared first on Towards Data Science.
Show HN: Galaxium, an experimental WebGPU space explorer
Comments
AI without an accountable owner is not innovation. It is exposure
An anonymous coding model, an autonomous killing claim, invisible chatbot memory, national guardrails, and China s job shock expose the Continue reading on Medium »
AI Made These 7 Businesses Cheaper to Test, Not Easier to Sell
The first version got cheaper. Finding a customer did not.Continue reading on Medium »
US nutrition startup Berry Street merges with India’s Healthify as GLP-1 trends upwards
Berry Street founder Noah Kotlove and Healthify founder Tushar Vashisht will act as co-CEOs of the new entity.
Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash
Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acceptance metrics, and show what determines whether speculation pays off. The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.
What my dad taught me about AI coding in the 90s
Comments
Anthropic’s new Claude Tag update lets its Slack agent read the full conversation — and jump in unprompted
Anthropic is making a calculated bet that the biggest bottleneck in enterprise AI isn t model intelligence — it s the fact that most people still use AI alone.In an exclusive interview with VentureBeat, Scott White, Anthropic s head of product for enterprise, laid out the company s thesis for what it calls multiplayer AI : a shift away from the single-user chatbot paradigm toward AI agents that operate across teams, read organizational context, and proactively insert themselves into work — sometimes without being asked.The strategy crystallized earlier this month when Anthropic updated Claude Tag, its agent that lives inside Slack channels, to read context across entire conversations rather than evaluating messages one at a time. According to the company, the change makes Claude roughly 30% better at deciding when — and, critically, when not — to jump into a conversation unprompted.That may sound like an incremental product update. White argues it represents something more fundamental:
How to Use Kimi K3: Moonshot AI’s 2.8T Open-Weight Model
Moonshot AI’s Kimi K3 is a 2.8-trillion-parameter open-weight model built with a Mixture-of-Experts architecture. It activates only a small fraction of its parameters per token, helping reduce inference costs while delivering strong coding and agentic performance. K3 combines near-frontier capabilities, open weights, and lower API pricing, making it an interesting alternative to proprietary models. In [ ] The post How to Use Kimi K3: Moonshot AI’s 2.8T Open-Weight Model appeared first on Analytics Vidhya.
AI Agents Don’t Need More Context — They Need Typed Context
AI agents don’t just have a context problem—they have a context typing problem. When instructions, memory, retrieved evidence, and tool outputs are flattened into one string, their semantic boundaries can disappear. I built a lightweight, zero-dependency Python runtime that keeps those boundaries explicit, tracks provenance, and rejects invalid context transformations before they reach the model. This article walks through the implementation, tests, and what this approach does—and does not—guarantee. The post AI Agents Don’t Need More Context — They Need Typed Context appeared first on Towards Data Science.
Dynamic Model Routing with an LLM Gateway
How CEL Routing Rules Decide Where Every AI Request GoesContinue reading on Data Science Collective »