A new personal finance experience in ChatGPT

#1 📝 OpenAI News

A new personal finance experience in ChatGPT - OpenAI introduced a new personal finance experience inside ChatGPT, aimed at helping users manage and understand their finances through the assistant. The launch appears to be a product feature update integrating financial tools and guidance into ChatGPT.

#2 𝕏

clem 🤗 launched Hugging Face Storage for model weights, datasets, checkpoints and artifacts—featuring simple per-TB pricing, built-in CDN, Xet deduplication and private-by-default settings.

#3 𝕏

xAI now lets users leverage their @grok subscription inside @NousResearch’s Hermes Agent, enabling direct access to Grok’s AI capabilities within the Hermes workflow.

#4 𝕏

Garry Tan built a defense-in-depth security stack for LLM apps, using Silmaril to block shell-level prompt injections, layered with OpenClaw container isolation and a Hermes Agent for runtime threat monitoring.

#5 📝 HumanLayer Blog

Context Forking to Save Time, Tokens and Trouble - Context forking is a primitive many coding agents (OpenCode, Pi, Claude Code) provide that lets you pop one or more user-message turns off a downwards-growing context window (stack) to restore an earlier state—rewinds happen at user-message boundaries, not mid-tool-call—and random-access edits are generally disallowed to avoid expensive cache misses, mangled accumulated context, and mismatches with agents’ internal file-read/write state. It’s used to course-correct agents, branch to explore different designs, or salvage high-quality context after context-inefficient operations (for example an agent reading ~40,000 tokens of output), and most implementations allow multiple forks and may snapshot code/disk state when rewinding.

#6 𝕏

Santiago previews a Higgsfield AI multi-model architecture that splits prompts into sub-tasks, routes each to the optimal specialist model, and unifies outcomes through a three-layer memory system—positioning it to outperform traditional single-model tools.

#7 𝕏

Santiago unveils MiniMax-M2.7, an open-weight model running at over 440 tokens/s, and provides a playground for testing its performance.

#8 📝 Simon Willison

datasette-llm-limits 0.1a0 - Released an alpha plugin (datasette-llm-limits) that integrates with datasette-llm and datasette-llm-accountant to configure per-user or global spending limits for LLM usage inside Datasette, with an example YAML configuration shown. The plugin helps manage usage windows and USD spending limits.

#9 📝 Simon Willison

QR code generator - Simon built a QR code generator tool (with help from Claude) for creating scannable codes for URLs, text, or WiFi networks, including a web form to enter network name, password, security type, and style options. A screenshot of the form and its fields is included.

#10 📝 PromptLayer Blog

What is agent evaluation — A practical guide for AI teams - Agent evaluation tests whether an AI agent reliably completes tasks across real inputs, edge cases, and versions by checking final outputs (black-box), the agent's steps (trajectory), and component behavior, using metrics like task completion rate, tool selection accuracy, unsupported-claim rate, latency/cost per step, and regression pass rate. PromptLayer claims to support this workflow with span-level traces, reusable datasets, batch evaluations, backtests against production history, regression testing, automatic evaluation triggers on new prompt versions, and flexible pipelines (code execution, human input, conversation simulation, equality/regex checks, and LLM assertions).

#11 𝕏

Garry Tan says you can token-max $10K/mo with OpenClaw/Hermes + GBrain to unlock 2028-level AI, effectively getting the future’s standard model now for about $100/mo.

#12 𝕏

Madhu Guru argues PMs trained to follow static playbooks must now invent new frameworks for AI products, as repurposed playbooks and A/B tests won’t deliver breakthroughs. He warns they need to unlearn mechanical methods and embrace original invention.

#13 𝕏

claire vo 🖤 urges leaders to be fully transparent about their AI goals and plans—“sunlight is the best disinfectant”—and trust their teams with honest dashboards instead of hiding details.

#14 𝕏

Lenny Rachitsky notes that top AI companies undergo major reorganizations every six months—mirroring traditional hypergrowth firms—and suggests this cadence may reflect human limits on change or the time required to spot broken processes.

#15 in

PDMA Seattle spotlights the growing gap between PMs who can build versus those who only spec, and invites you May 27 (12–1 PM PST) to join AI/Product Coach Bonnie Yu for a free 60-minute live demo using Claude Code to take an idea from spec to working prototype.

#16 in

Marc Baselga observes that AI orgs are collapsing traditional ladders as dozens of PMs drop into “super IC” roles—VPs now work directly with a handful of senior PMs who own entire business lines.

#17 𝕏

v0 launched Browser Use, enabling it to open and interact with the apps it builds and critique their designs. It can also debug complex flows, proactively fix issues, and share real-time screenshots of its progress.

#18 𝕏

bolt.new renamed its tiers—Standard is now “Sonnet” and Max is “Opus”—but you’re still accessing exactly the same models as before, just with cleaner naming for selection.

#19 𝕏

Cognition launched a comprehensive DeepWiki deep-dive into X’s latest algorithm, revealing that while engagement weights (likes vs. replies) remain private, maximizing dwell time is the clearest way to boost post visibility.

#20 📝 Claude Code Blog

Deploying Claude across the legal industry - An article about deploying Claude in legal organizations, covering use cases and product offerings (Claude Cowork) for the legal industry and Enterprise AI teams. It highlights how Claude can be integrated and adopted across legal workflows.

#21 📝 PromptLayer Blog

LLM as a judge — How do you know if your AI is actually good? - Using an LLM as a judge lets teams automatically score and compare model outputs—cutting prompt-iteration feedback loops from days to minutes—and is already used in tools like OpenAI Evals, LangSmith, and PromptLayer Evaluations. But judge models inherit biases (they often prefer longer answers, can be inconsistent and phrasing-sensitive), so the article argues for detailed rubrics and combining LLM evaluators with heuristics, human review, and application-specific checks as part of continuous evaluation infrastructure (including RLAIF and hallucination-catching) to reliably detect regressions.

#22 𝕏

Madhu Guru notes AI product patterns are evolving far faster than previous tech shifts, so there’s no stable meta to copy—and he expects this dynamic to persist.