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Here's the must-read: | | Together with: | | | | Today's Author Lior Alexander. Founder of AlphaSignal and former ML Engineer. Previously built ML systems at Iguazio, Guesty, Enphase, Mila. | | Top News | | | | Google introduces vibe design in Stitch with voice control, infinite canvas, and DESIGN.md design system support | | 23,458 Likes | | Google updates Stitch to generate full UI prototypes from simple prompts and connect them into working app flows. You describe an app, and Stitch creates screens, links them, and lets you preview interactions with a single click. The key change is DESIGN.md, a plain-text file that defines your design system so every screen follows the same visual rules. Stitch solves a common issue where generated UI screens look inconsistent or disconnected. It gives you a shared, editable source of truth that both you and the model use. Key features -
Generates multiple UI screens and connects them into a navigable flow automatically -
Infers navigation logic by mapping buttons and links to likely destination screens -
Creates next screens from clicks using model-based context understanding -
Defines design rules in DESIGN.md using simple markdown (colors, fonts, components) -
Applies consistent styling across all generated screens using shared design tokens -
Supports version control since DESIGN.md is a plain text file | | | | Presented by Mistral AI | | Mistral Vibe Cuts Pull Request Time in Half | | You know that feeling when boilerplate eats your whole afternoon? Mistral Vibe is a terminal-native coding agent. It knows your entire codebase before it writes a single line. What it handles for you: -
PRs, tests, and docs on autopilot -
Full codebase refactors to modern stacks -
Fine-tune it on your own repos and conventions -
Open-source, MIT and Apache 2.0 licensed The latest version introduces custom subagents and slash-command skills | | | | partner with us | | Top Paper | | | | Princeton researchers introduce OpenClaw-RL1, enabling agents to learn continuously from real interactions using asynchronous RL | | 1,621 Likes | | OpenClaw-RL1 trains AI agents directly from real usage instead of offline datasets. You deploy a self-hosted model, route it through an RL server, and every interaction becomes training data. The system uses next-state signals (user replies, tool outputs, environment changes) as feedback. It updates the model in the background while it serves requests, so learning never blocks usage. This targets a core limitation in RL for LLMs: static training pipelines that ignore real-world interaction data. Key features -
Convert chats, tool calls, and environment outputs into structured training trajectories -
Use binary rewards (good/bad) from PRM voting across multiple evaluations -
Apply on-policy distillation (OPD) with textual hints for token-level corrections -
Run fully asynchronous pipeline with separate serving, scoring, and training workers -
Combine scalar rewards and token-level signals into a single optimization objective Performance Results Experiments compare binary RL, OPD, and combined training under iterative updates. -
Binary RL reaches 0.25 at 8 steps and 0.23 at 16 steps -
OPD reaches 0.25 at 8 steps and improves to 0.72 at 16 steps -
Combined method reaches 0.76 at 8 steps and 0.81 at 16 steps -
Combined signal merges scalar evaluative reward with token-level directional guidance | | | | Presented by Datadog | | Your Always-On AI Teammate | | What if incident response started before your team even logged in?
Learn how Bits AI SRE automates investigations, connects logs and telemetry, surfaces likely root causes, and drafts summaries and follow-ups. | | | | partner with us | | Top News | | | | MiniMax launches M2.7, a model that writes its own training code and improves through iterative feedback loops | | 2,728 Likes | | MiniMax released M2.7, a model that takes part in its own training by writing code, testing itself, and improving over time. Instead of relying only on static datasets, it runs repeated cycles where it finds mistakes, fixes them, and updates how it learns. This shifts training from a one-time process to a continuous loop where the model helps build better versions of itself. What it does -
Runs 100+ self-improvement cycles, analyzing failures and rewriting its own training logic -
Builds internal test sets from real task errors instead of fixed benchmarks -
Improves accuracy by ~30% through iterative self-correction loops -
Reduces incident recovery time to ~3 minutes in some production scenarios Performance -
Scores 56.22% on SWE-Pro for real-world coding task completion -
Reaches 57.0% on Terminal Bench 2 for command-line task execution -
Achieves 97% skill adherence across 40+ tool-based workflows -
Supports multi-agent setups where agents coordinate on shared tasks | | | At Alpha Signal, our mission is to build a sharp, engaged community focused on AI, machine learning, and cutting-edge language models, helping over 200,000 developers stay informed and ahead. We're passionate about curating the best in AI, from top research and trending technical blogs to expert insights and tailored job opportunities. We keep you connected to the breakthroughs and discussions that matter, so you can stay in the loop without endless searching. We also work closely with partners who value the future of AI, including employers and advertisers who want to reach an audience as passionate about AI as we are.
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