Anthropic Opens Claude Interactive Charts and Diagrams Beta

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Today's top 25 insights for PM Builders, ranked by relevance from X, YouTube, LinkedIn, and Blogs.

Anthropic Opens Claude Interactive Charts and Diagrams Beta

#1 š•

Claude now builds interactive charts and diagrams directly in-chat, rolling out in beta across all plans—including free.

#2 š•

Philipp Schmid rolled out experimental spend caps for the Gemini API—limits take up to 10 minutes to activate (email alerts coming soon)—and asked users to set caps and share feedback.

#3 š•

Sundar Pichai launched Ask Maps, an AI-powered chat interface in Google Maps for personalized recommendations and route planning, and unveiled immersive 3D navigation with Street View overlays for real-time guidance.

#4 ā–¶ļø

Replit Agent 4 Is Here: Everything You Need to Know

Peter Yang

Building and publishing a full-stack mobile habit-tracker app on Replit using Agent 4 to generate design variants on an infinite canvas, build calendar and habits tabs with parallel AI agents, and deploy with one-click publishing.

  • Four design variants (Glass Morphic, RPG Quest, Botanical, surprise) were generated on Replit Agent 4’s infinite canvas in about two minutes.
  • Two AI agents built the calendar and habits tabs in parallel, completing coding in roughly five minutes and tracked via a Trello-style taskboard with draft, active, ready, and done columns.
  • The full-stack app (frontend, backend, database) was published on Replit with a built-in security scan and deployed in one go.

#5 š•

Mustafa Suleyman launches Copilot Health, a secure AI-powered hub that connects users’ EHR records and wearable data to deliver personalized medical insights and proactive health nudges, heralding the dawn of medical superintelligence.

#6 š•

AI at Meta launched Canopy Height Maps v2 (CHMv2), an open-source, high-resolution global forest canopy mapping model co-developed with @WorldResources that leverages our DINOv3 Sat-L vision model to deliver major gains in accuracy, detail, and consistency.

#7 š•

Google Research expanded its global flood forecasting coverage by launching an AI-driven urban flash flood forecasting system, combining high-resolution precipitation nowcasts with hydrological models to deliver real-time, city-scale flood alerts.

Also covered by: @Google Research

#8 š•

Sundar Pichai rolled out an AI flood‐forecasting model that predicts urban flash floods up to 24 hours ahead, backed by Groundsource—a Gemini‐driven dataset of 2.

Also covered by: @Google Research

#9 š•

Sebastian Raschka launched a ā€œBuild an LLM From Scratchā€ YouTube series, walking through data prep, transformer architecture implementation, training and fine-tuning to create custom large language models.

#10 š•

LlamaIndex šŸ¦™ launched ā€œagenticā€ OCR, a goal-driven, multimodal document parser that uses visual grounding and self-correction loops to adapt to layout changes and trace every field back to its source.

#11 ā–¶ļø

7 new open source AI tools you need right now…

Fireship

Demonstrates seven open-source AI tools—Agency, Prompt Fu, Mirrorish, Impeccable, Open Viking, Heretic, and Nano Chat—to streamline AI agent orchestration, prompt testing, prediction engines, UI design, context management, model de-censoring, and custom LLM training.

  • Prompt Fu acts as a unit-testing framework for prompts, benchmarking them across different models and performing automated red-team attacks to expose prompt injection vulnerabilities.
  • Impeccable includes 17 front-end design commands—such as distill to simplify interfaces, colorize to apply brand palettes, and animate plus delight for custom UI animations—to rapidly improve app design.
  • Nano Chat implements the full LLM pipeline (tokenization, pre-training, chat fine-tuning, evaluation, and a web UI) and can train a small language model for about $100 in GPU time.

#12 š•

There's An AI For That highlights Modulate.ai’s new, cost-effective speech-to-text API, complete with a built-in comparison tool against Deepgram, ElevenLabs, and AssemblyAI.

#13 š•

Cursor introduced a novel scoring method for agentic coding tasks, benchmarking its models on intelligence and efficiency. The framework’s comparative results reveal each model’s performance trade-offs.

#14 š•

Tal Raviv looped Claude into a weekend notification-design brainstorm by holding down the dictation button to feed it bottom-line points in real time, and Claude’s targeted questions kept their creative momentum flowing.

#15 in

Marc Baselga recommends quantifying the cost of slow AI adoption (missed markets, lost deals, compliance delays) and enlisting a senior IT- or C-suite sponsor to push for safe approval of broader AI tools beyond just Copilot.

#16 š•

Guillermo Rauch launched ā€œAgent-native Flagsā€ in the Vercel CLI—a new CLI + Skill integration that lets you manage feature flags programmatically and is optimized for AI agents.

#17 š•

Santiago keeps each Claude chat in its own terminal tab per project and uses claude --continue to pick up after closing or --resume [session id] to jump to specific sessions, all without plugins.

#18 š•

Santiago applauds an SMS-based AI assistant that forgoes inbox access and full upfront data dumps, instead building context incrementally from your actions—unlike most AI helpers.

#19 in

Wade Foster spotlights Claire Vo’s no-code ā€œSunday Scariesā€ agent—built in an afternoon with Zapier and a prompt to auto-prep her calendar, meetings, and school drop-offs—driving ChatPRD to 100k+ users with just her and one engineer.

#20 š•

Yann LeCun says their planning world models are action-conditioned (hence truly causal), reviving 1950s optimal-control ideas—and that training such models from raw sensory inputs like video demands new techniques.

#21 in

Dharmesh Shah is proposing a simple, standard ā€œFile System Protocolā€ that lets AI agents navigate and use hierarchical data stores—arguing that, like with CLI tools, coding agents already excel at understanding and searching file-system structures.

#22 š•

Aravind Srinivas says Slack will become the enterprise AI interface and Perplexity Computer slots in naturally. He predicts future multi-billion- and trillion-dollar Slack-based companies will delegate more tasks to AI than to humans.

#23 šŸ“ Simon Willison

Coding After Coders: The End of Computer Programming as We Know It - Clive Thompson's NYT Magazine feature examines AI-assisted development, interviewing many software developers; Simon Willison appears as a source highlighting that programmers can tether AIs to reality by requiring tests. The overall tone captures the industry's changes and leans toward cautious optimism.

#24 šŸ“ Simon Willison

Grief and the AI Split - Les Orchard argues that AI-assisted coding exposes an existing divide among developers: some will embrace machines to direct work, others will continue hand-crafting code for the craft itself. The post frames this as a visible fork in developer motivations.

#25 š•

Guillermo Rauch: Vercel now powers Notion’s agent platform via Notion Workers on Vercel Sandbox, merging natural-language specs with data integrations. He believes this positions Notion as one of the big winners in the agent era.

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