| Stay updated with today's top AI news, papers, and repos.  Hey James, Your daily briefing is ready. You can finally take a break from the AI firehose.
Our algos spent the night splitting signal from noise and pulled the top news, models, papers, and repos.
Here's the must-read: | | Top News | | | | Anthropic releases SCONE-bench, a reproducible blockchain testbed that measures exploit success in dollars | | 3,423 Likes |  | | Anthropic wanted to know how far an AI can go when you drop it into a blockchain simulator. That question led to SCONE-bench, a testbed that recreates real smart-contract exploits and measures outcomes in dollars. The headline arrives fast: frontier agents extract $4.6M from contracts deployed after their March 2025 cutoff. Smart contracts lock up real funds, and one bug can drain everything. SCONE-bench gives you a sandboxed blockchain fork, the contract code, and one task, find the flaw and write the exploit. Each run uses Docker for clean, reproducible execution. The breakthrough comes from how well agents use tools to inspect code, run transactions, and retry failures. Some models even maximize revenue by targeting every affected liquidity pool. You can use SCONE-bench to audit contracts before deployment: load your contract, run the agent, review the exploit script, and measure the financial impact in a controlled chain. | | | | Exclusive offer for the AlphaSignal community: 50 free tickets to the most chaotic event at AWS re:Invent | | Sponsored | | Bright Data is hosting "Unlocking the Bots", a live BattleBots arena showdown on Dec 2, 6:30 PM, at the official BattleBots arena in Las Vegas. Expect sparks, flying metal, and full-stack destruction. You'll eat, drink, and hang with real combat robots. Meet the builders. Watch teams repair under pressure. Maybe even step into the ring and operate a hammer bot yourself. - 6:30 PM: Food, drinks, bot demos (parking lot)
- 7:15 PM: Meet the builders (workshop)
- 7:30 PM: Live battles begin (arena)
- 8:15 PM: Swag + wrap
First come, first served. | | | | partner with us | | Top Paper | | | | Runway announces Gen-4.5, its strongest model for cinematic and realistic visuals | | 2,799 Likes |  | | Runway's Gen-4.5 arrives like a quiet engineering flex: a video model that moves objects with believable weight and follows your most specific camera instructions. Video models often struggle with motion, timing, and detail. Gen-4.5 raises the bar with an Artificial Analysis Text-to-Video score of 1,247, the highest recorded so far. The problem starts with older models that lose detail across frames or mis-handle physics. Runway leans on new training and post-training methods to tighten motion behavior and visual consistency. The breakthrough shows up when you prompt it for multi-step scenes, Gen-4.5 handles them without drifting off-script. You use it through the Runway platform. Select Text-to-Video, Image-to-Video, Keyframes, or Video-to-Video and provide a clear prompt. Features and results -
Produces stable motion with accurate object weight and surface behavior. -
Maintains fine details like fabric texture and hair across frames. -
Executes sequenced events and camera moves inside a single prompt. -
Runs on NVIDIA Hopper and Blackwell GPUs with Gen-4-level speed. | | | | Top News | | | | Hugging Face launches Transformers v5, reorganizing all models into clearer, modular components | | 1,582 Likes |  | | Transformers v5 arrives from Hugging Face as a long-awaited cleanup for the AI ecosystem. The library grew from dozens of models to hundreds, and maintaining it started to feel like stitching together mismatched parts. The team stepped back and asked a simple question: what if every model behaved the same way end to end?This drove a full redesign. Transformers v5 restructures architectures into smaller modules, so you read model logic without digging through noise. A new AttentionInterface acts as a unified switchboard for attention types like FA1/2/3, FlexAttention, and SDPA, which removes scattered implementations. PyTorch becomes the single backend, while inference gains continuous batching, paged attention, and an OpenAI-style server. Key features: - Modular components simplify new model integrations and code maintenance.
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Unified attention interface controls multiple attention backends. -
New inference APIs support high-volume requests with cleaner defaults. -
Quantization loads 8-bit and 4-bit weights and exports GGUF. How to use it:
Import normally, set configs for attention or quantization, and launch transformers serve
to deploy an endpoint. | | | 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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