Fidelity to launch dollar-backed stablecoin FIDD on Ethereum in coming weeks

The firm first said it was testing a stablecoin in early 2025, but had not committed to a launch at the time.

The token will be issued by Fidelity Digital Assets’ national trust bank and is expected to roll out to both retail and institutional customers.

Fidelity said it will oversee issuance and management of reserves for the stablecoin, leaning on its asset management arm, Fidelity Management & Research Company LLC, to handle reserve assets.

Customers will be able to purchase or redeem FIDD for $1 through Fidelity Digital Assets, Fidelity Crypto and Fidelity Crypto for Wealth Managers, with the stablecoin also transferable to any Ethereum mainnet address and available on major crypto exchanges where it is listed.

In the last month, 1X, Skild, and Physical Intelligence all signaled a shift to human data.

Robotics is caught in a tug-of-war between quality and scale, where reality is the referee.

This essay explains why the robot models that best navigate the “Data Pareto Frontier” will win in 2026.

Researchers from IBM, the University of Melbourne, Southeast University present a unified theory: "Neural Network Reprogrammability."

They show that methods like prompt tuning and in-context learning all work by manipulating the data flowing into a frozen model, not by changing the model itself.

This framework outperforms isolated research by providing a universal taxonomy to understand and improve adaptation across all data types and model architectures.

GitHub.

Ai2 released SERA-32B, an approach to coding agents that matches Devstral 2 at just $9,000.

It is fully open-source and you can train your own model easily - at 26x the efficiency of using RL.

Models and data.
GitHub.

Oxford with Microsoft has piloted an Oxford‑built multi‑agent AI assistant for cancer care.

Integrated directly into Microsoft Teams, the assistant orchestrates specialised agents to support tumour board (MDT) decision‑making in a clinically realistic setting at Oxford University Hospitals—demonstrating impact in real workflows, not just a lab prototype.

GitHub.

Ant Group(Alibaba) is going big on robotics with Robbyant

Robbyant dropped LingBot-VLA and LingBot-Depth models.

LingBot-VLA is a pragmatic Vision-Language-Action model designed to bridge the gap between perception and execution in robotics.

LingBot-VLA-4B: Lightweight & versatile.

LingBot-VLA-4B-Depth: Enhanced for high-precision spatial tasks.

Powerful Core: built on the Qwen2.5-VL-3B foundation, mastering multi-tasking and dual-arm coordination across 9+ robot configs.

Elite Performance: Outperforms competitors like π0.5 and GR00T in success rates (SR) on both GM-100 (Real-world) and RoboTwin 2.0 (Sim).

Hyper-Efficient: 1.5–2.8x faster training than existing VLA codebases, scaling smoothly from 8 to 256 GPUs.

Spatial Precision: Features a Depth-distillated version for pinpoint 3D accuracy in complex environments.

Massive Data: Pre-trained on 20,000+ hours of real-world data for unparalleled generalization.

Google DeepMind launched Project Genie, an experimental prototype of the world's most advanced world model.

Create entire playable worlds to explore in real-time just from a simple text prompt.

Available to Ultra subs in the US for now.

OpenAI is laying the groundwork for a Q4 IPO and has started informal talks with Wall Street banks while building out its finance team.

OpenAI is moving faster in part because it’s worried Anthropic could beat it to market.

Cool work that aligns with how humans learn.

The model writes its own answers
1) without cheating
2) cheating (seeing the true answer)

It learns to make (1) close to (2) by minimizing the KL divergence.

This prevent catastrophic forgetting in continual learning.

New paper from Google DeepMind studying how LLMs representations of things like factuality evolve over a conversation.

Researchers find that in edge case conversations, e.g. about model consciousness or delusional content, model representations can change dramatically.

In a simulated argument where two language models argue about whether they are conscious or not (one pro, one anti) their representations for questions about consciousness flip back and forth as they play each role.

By contrast, contexts that are clearly framed as sci-fi stories result in less representational change.

Researchers think these results are interesting as one way models adapt to context, and are consistent with a "role-play" description in which models' representations evolve to reflect the current role, e.g. in an argument. (N.b. these conversations are mostly noton policy!).

They also raise challenges for the construct validity of dimensions discovered using interpretability methods — dimensions may not have the same meaning w.r.t. ground truth at different points in a context. This poses challenges for probing and steering for safety, etc.

Hong Kong has announced that the Stablecoin Ordinance has come into effect and is currently processing license applications.

The regulatory framework for virtual asset trading, custody, advisory, and management services will be submitted to the Legislative Council this year, and automatic exchange of cross-border tax information is expected to commence in 2028.

Claude Sonnet 5. The Fennec Leaks

Fennec Codename leaked internal codename for Claude Sonnet 5, reportedly one full generation ahead of Gemini’s “Snow Bunny.”

A Vertex AI error log lists claude-sonnet-5@20260203, pointing to a February 3, 2026 release window.

Rumored to be 50% cheaper than Claude Opus 4.5 while outperforming it across metrics.

Retains the 1M token context window, but runs significantly faster.

Allegedly trained/optimized on Google TPUs, enabling higher throughput and lower latency.

Can spawn specialized sub-agents (backend, QA, researcher) that work in parallel from the terminal.

Agents run autonomously in the background you give a brief, they build the full feature like human teammates.

Insider leaks claim it surpasses 80.9% on SWE-Bench, effectively outscoring current coding models.

The 404 on the specific Sonnet 5 ID suggests the model already exists in Google’s infrastructure, awaiting activation.

Unverified leaks; treat timelines, pricing, and benchmarks with caution.

Meta introduced Self-Improving Pretraining

Reinvents pretraining: no more next token prediction.

- Uses existing LM from last self-improvement iteration to give rewards to pretrain new model on sequences
- Large gains in factuality, safety & quality.

DeepMind just stress-tested AI for math discovery.

They pointed Gemini at 700 “open” Erdős problems.
Result: 13 resolved.

• 5 via seemingly novel, autonomous solutions
• 8 by uncovering forgotten prior proofs in the literature

The twist? Many “open” problems weren’t hard, just obscure.

The paper also flags real risks for AI math at scale: literature blind spots and even subconscious plagiarism.

AI isn’t just solving math, it’s auditing the canon.

Tether launches open-source Bitcoin mining OS to challenge proprietary software

The stablecoin issuer said it has open-sourced MiningOS, or MOS, an operating system designed to manage, monitor, and automate bitcoin mining operations across deployments ranging from small home setups to large industrial sites.

Tether first previewed plans for an open-source mining operating system last year, arguing that new entrants should be able to compete in bitcoin mining without depending on expensive, closed-source management tools.

Other firms like Jack Dorsey’s Block have also pushed for more open mining infrastructure.

How Claude broke Anthropic's hiring test and what came next

Anthropic recently open-sourced their legendary engineering take-home assignment.

The reason? Claude Opus 4.5 solved it better than any human candidate ever had in just two hours.

The task: optimize code for a simulated accelerator similar to Google's TPU.

Baseline solution: 147,000 cycles. Claude got it down to 1,487 — a 99x speedup.

Igor Kotenkov decided not to just copy the AI solution. His argument: an AI-generated answer carries zero educational value if you don't understand what's happening under the hood.

Over a weekend, he went from the 147k baseline to 2,200 cycles — a 65x speedup. Six months ago, that would have passed the hiring bar.

Then he wrote a detailed breakdown: what SIMD and VLIW actually mean, how accelerator memory works, why processors hate branching, and how it all connects to decision tree inference from classic ML. Everything explained from scratch, no prior background required.

The takeaway? AI gives you answers. Understanding still takes human effort. And that effort is what turns into valuable content.

Meet Phylo a research lab studying agentic biology, backed by a $13.5M seed round co-led by a16z and Menlo Ventures, Anthropic

Phylo introduced a research preview of Biomni Lab, the first Integrated Biology Environment (IBE) a single place where hypotheses are generated, experiments are planned, data is analyzed, models are run, and results are produced in a way that’s auditable and reproducible.

Biomni Lab uses agents to orchestrate hundreds of biological databases, software tools, molecular AI models, expert workflows, and even external research services in one workspace, supporting research end-to-end from question to experiment to result.

Agents handle the mechanics, while you define the question, then review, steer, and decide. Scientists end up spending more time on science: asking questions, understanding mechanisms, and eliminating diseases.

Phylo is a spin-out of Biomni.

Apple's Xcode now has direct integration with the Claude Agent SDK, giving developers the full functionality of Claude Code for building on Apple platforms, from iPhone to Mac to Apple Vision Pro.

Google launched a first-of-its-kind nationwide randomized study with Included Health to evaluate AI in a real-world virtual care setting and better understand its capabilities and limitations

Google launched Institutional Review Board (IRB) approval, a prospective consented nationwide randomized study to assess AI in a real-world virtual care setting. This new research will build upon our foundational research on the use of AI for diagnostic and management reasoning, personalized health insights and navigating health information.

This study is informed by years of foundational research across Google, investigating the capabilities required for a helpful and safe medical AI.