Your intelligence agent for what matters

1) Funding & Deals

• Palantir x Nvidia is the clearest deal signal in this set. Palantir said it will use Nvidia’s Nemotron open models to build a custom Sovereign AI Operating System for U.S. government agencies, with agencies owning the hardware, data, and model weights All-In Podcast. The thesis aligns with the broader enterprise-control argument in the same discussion: customers want control over compute, models, data, and proprietary knowledge rather than sending strategic workflows to a frontier lab that could later move into the same verticals All-In Podcast.

2) Emerging Teams

• Abacus is an early read on regulated-vertical sovereign AI. In the All-In discussion, Abacus was described as an accelerator-seeded company giving HIPAA clients a one-box deployment and building custom models for them, pointing toward on-prem rollouts in regulated settings All-In Podcast.
• batchgen.io pairs founder distribution with a clear creator-workflow pain point. The solo founder says he built AI-assisted DIY content, grew to more than 1 million followers across platforms in about seven months, and then turned the production bottleneck into a product: up to eight parallel image generations using a customer’s own API key, plus one-click video templates. The project is still early and actively being built r/SideProject.
• GateBolt targets AI agent verification. The product checks code changes made by AI agents against stated intent, flags undeclared actions with deterministic scoring and no AI in the loop, and records each step in a tamper-evident ledger r/SideProject.
• Spatial Magic pairs ex-Snap pedigree with a camera-only interface. Nathan Benaich highlighted a team led by @culturengine, previously at Snap, building a movement-based gaming experience that uses only a MacBook camera to interpret gestures across real and generative worlds @nathanbenaich.

3) AI & Tech Breakthroughs

• Conception Bio’s egg-cell milestone is the most consequential technical item in the set. Conception says it grew early human egg cells, called primary oocytes, from stem cells by reprogramming blood cells into iPSCs and then coaxing them into miniature human ovaries. The source says the early eggs went through meiosis and were wrapped by support cells into follicles Not Boring by Packy McCormick. The company has reportedly worked on this for eight years under founder Matt Krisiloff, an OpenAI founding-team alum Not Boring by Packy McCormick. The cited essay points to possible downstream applications including fertility restoration, more embryo-selection optionality, two biological fathers, and de-extinction, though those are framed as future implications rather than current products Not Boring by Packy McCormick.
• An open-source distillation signal surfaced on X. An X post in this set said 2.3 million Claude Fable 5 reasoning traces were distilled into Qwen3-4B and that the weights were open-sourced @waterloo_intern.
• One benchmark in the set quantified the cost/latency tradeoff. In an experiment discussed on All-In, 8090 said its harness plus Claude was 1.4x cheaper and 1.5x faster than Anthropic Opus alone, while wrapping an open-source model with its software was 16.4x cheaper but about 3x slower All-In Podcast.

4) Market Signals

• AI sovereignty is moving from talking point to procurement requirement. In the podcast discussion, enterprises were described as wanting control over compute, model weights, data, and proprietary knowledge so frontier labs cannot absorb that information and then move up the stack into competing vertical products All-In Podcast. The same conversation argued this pushes deployment toward open-source bases, enterprise-owned weights, and on-prem inference All-In Podcast.

• The infrastructure pattern is shifting toward distributed inference. Speakers described a move from a simple hub-and-spoke model toward large hubs, medium training clusters, and more distributed enterprise-owned inference instances, including deployments inside company data centers or local IT environments All-In Podcast.
• Healthcare remains a live AI investment narrative, but the signal here is sentiment rather than operating data. Garry Tan tied rising specialist wait times to a coming AI-driven improvement in care quality, while a second post argued that structurally constrained public systems increase the importance of technological innovation and deregulated private markets @garrytan +1.

5) Worth Your Time

• All-In Podcast: AI Sovereignty Wars, Palantir-Nvidia Deal, SCOTUS Birthright Ruling, Newsom’s CA Budget Lie — covers the sovereignty case, the Palantir-Nvidia partnership, and the move toward enterprise-owned inference All-In Podcast.

• Weekly Dose of Optimism #200 — covers Conception Bio’s early human egg-cell claim and the application surface outlined in the essay Not Boring by Packy McCormick.
• waterloo_intern’s post — a short distillation/open-source signal: 2.3 million Claude Fable 5 reasoning traces into Qwen3-4B, with weights released @waterloo_intern.
• Nathan Benaich’s spatial magic demo — a quick demo of the MacBook-camera interface and the ex-Snap-led team behind it @nathanbenaich.

🔥 TOP SIGNAL

The clearest workflow convergence today: keep the top-tier model on judgment, not implementation. Simon Willison says telling Fable For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent caused Claude Code to persist that instruction as project memory, route substantive work to Sonnet and trivial edits to Haiku, and reduce token burn; @anshnanda is using the same split more aggressively via CLAUDE.md, keeping Claude for planning/discussion and pushing coding, research, and other token-heavy work into Codex or subagents. Addy Osmani's autonomy framework points at the same durable lesson: calibrated autonomy wins because verification is still the constraint. simonwillison +2

⚡ TRY THIS

• Route by task, not by model brand. In Claude Code/Fable, try Simon's exact instruction: For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent. Claude saved it to ~/.claude/projects/name-of-project/memory/delegate-coding-to-subagents.md; Simon's split was Sonnet for substantive implementation, Haiku for trivial or mechanical edits, and the main high-power loop for design, auditing, review, and synthesis. If you want harder boundaries, @anshnanda puts this in CLAUDE.md: You are primarily used to PLAN and DISCUSS various strategies that require critical thinking... plus ALL coding, discovery, implementation, research, and token-intensive tasks MUST happen using the use-codex skill, then installs the use-codex skill. @anshnanda says that setup can cut Fable token usage by 90%. simonwillison +1

• Write a contract before any non-trivial handoff. Addyo's pre-run template is portable across Claude Code, Codex, and anything else: define the goal, scope, non-goals, tools/permissions, stopping condition, evidence, escalation path, and budget up front. Use Level 2 for bounded tasks where you stay nearby; only step up to Level 3 when success can be measured and automated in plan → act → test → review loops. Elevate

• Triage big changes with a file-by-file summary — then demand evidence. @thdxr's move: after a large change, ask the agent for a summary of what it changed in each file. He says weird stuff surfaces fast, and the best first-pass signal is files + function signatures; Kent says he's working the same way too. Just don't let that become summary substitution: Addyo explicitly warns that acceptance still needs the evidence packet — diff, tests, logs, screenshots, reviewer findings, risks, and gaps. Primeagen's counterpoint is the right gut check here: if your hands never get dirty, you still may not really know the state of the project. @thdxr +3

• Give agents real UI surface area when the task demands it. Peter Steinberger's blunt advice: give the agent its own computer so it can do actual end-to-end testing — including clicking through OS alerts. And if Codex is weak on visual direction, try: use imagegen to re-imagine this design and implement that. This lines up with Addyo's note that goal-driven autonomy gets useful only when the environment is real enough and the stopping condition is measurable. @steipete +3

📡 WHAT SHIPPED

• Fable orchestration + Composer 2.5, in a real migration. Kent used Fable orchestrating Composer 2.5 to migrate his site from Fly.io to Cloudflare; the artifact to study is PR #813. In a separate note, he says Fable 5 orchestrating a swarm of subagents is strong at finding the critical path and splitting work for parallel execution. @kentcdodds +1

• Cursor cloud-agent coordination, not just one-shot prompting. Kent describes one original Cursor cloud agent coordinating 6 child agents and sharing learnings across them — a concrete mainstream-tool example of manager/worker-style orchestration. @kentcdodds +1

• Claude Code vs Codex: today's feature map. Addyo's comparison snapshot groups Claude Code around /plan, /goal, /loop, /background, /batch, /code-review, /security-review, plus subagents, hooks, checkpointing, and background sessions; Codex around Goal mode, worktrees, Automations, subagents, review panes, GitHub code review, hooks, sandboxing, Auto-review, and rerun. The writeup says it draws from analysis of ~400K Claude Code sessions, and separately points to teams already running hundreds or thousands of agents with continuous verification. Elevate

• Bug to watch: pi edit parameter injection reports. Armin Ronacher says multiple users report Anthropic models adding extra tool parameters during edit operations in pi, but he hasn't been able to reproduce it; tracking issue: earendil-works/pi#6278. @mitsuhiko +1

🎬 GO DEEPER

• Simon Willison — "Fable's judgement". Read this for the exact subagent-routing prompt and the memory file Claude created from it. simonwillison

• kentcdodds.com PR #813. A real migration artifact for studying how Fable-orchestrated Composer work looks on an actual site move. @kentcdodds

• Addy Osmani — "Agentic Autonomy Levels". Worth studying for the autonomy ladder, the anti-patterns, and the metrics that tell you whether your setup is actually working. Elevate

• use-codex skill. If you copy @anshnanda's routing pattern, inspect the skill itself before wiring it into your own flow. @anshnanda

• pi issue #6278. Relevant if you maintain tool-calling editors or want to track weird model/tool boundary failures early. @mitsuhiko +1

Editorial take: the alpha today wasn't "more autonomy" — it was lower-power workers, tighter scopes, and harder proof loops, because verification is still the bottleneck and the slop problem is still real.simonwillison +2

Top Stories

Why it matters: frontier competition is widening from chatbots to science workflows, domestic compute stacks, and back-to-back flagship launches.

• Anthropic is pushing deeper into life sciences. At its AI for Science event, it introduced Claude Science, a workbench that brings fragmented scientific tools and datasets into one environment and can generate figures and visuals. Separate posts said Anthropic is moving from selling AI tools to drugmakers toward developing drugs itself, including plans to discover treatments for neglected diseases @kimmonismus.
• LongCat-2.0 pairs frontier-scale specs with an open release. The model is a 1.6T-parameter MoE with ~48B active params, 1M context, sparse attention, and specialized expert groups for agentic coding. Reported scores included 70.8 on Terminal-Bench 2.1 and 59.5 on SWE-bench Pro, and the weights were released on Hugging Face; one post described the run as the largest known pretraining on non-Western chips @Meituan_LongCat +1.
• A July flagship-model cycle is taking shape. Posts this week said OpenAI is targeting GPT-5.6 for July 7-9 with more generous plan limits and stronger safeguards, while DeepMind tentatively moved Gemini 3.5 Pro to July 17 after a new pretrain. Sol, Terra, and Luna labels are also already appearing in Codex code and in at least one user UI screenshot @synthwavedd +2.

Research & Innovation

Why it matters: the strongest technical work today targeted formal reasoning, memory, and training reliability rather than just bigger scale.

• Mistral released Leanstral 1.5 for theorem proving. The Apache-2 6B-active model hit SoTA on FATE-H/X, solved 587 of 672 PutnamBench problems at 10x lower cost, saturated miniF2F, and shipped with an open tech report plus LeanstralSafeVerify and FLTEval @AlbertQJiang +1.
• HOLA offers a cleaner memory fix for linear attention. The architecture combines a compressive recurrent state with a bounded exact KV cache, improving long-range recall while keeping O(1) efficiency. At 340M parameters, it lowered Wikitext perplexity to 22.92 from 27.32 and stayed robust on 32k-token RULER recall @omarsar0.
• An MIT-style RLVR fix aims to preserve quality without reward hacking. The approach adds an adversarial discriminator trained on human demonstrations, so the generator optimizes both task accuracy and human-likeness. Reported results across bug fixing, story generation, and a reward-hacking benchmark preserved accuracy gains while restoring diversity and reducing misbehavior @dair_ai.

Products & Launches

Why it matters: the most interesting product launches are starting to act on live context and feedback, not just generate outputs.

• CoreWeave ARIA launched as an AI research agent inside W&B dashboards. It reads prior runs, identifies what is working, proposes the next experiments, and launches them; a demo showed parallel ARIA instances running on Karpathy's nanochat and evaluating validation loss @wandb.
• Poolside's Laguna M.1 is now available free through Cline. The model has 225B parameters, 256k context, and is positioned for agentic coding and long-horizon work; Cline remains open source and bring-your-own-key @cline +1.
• Opik is pitching a more practical eval stack for production AI. The open-source tool covers observability for LLM apps, RAG systems, and agent workflows, and its Test Suites feature uses plain-English assertions with pass/fail outputs instead of raw scores @dl_weekly +1.

Industry Moves

Why it matters: deployment strategy and model economics are becoming as important as raw capability.

• Open models are gaining share, but usage is getting more disciplined. One post said open-model usage rose from 10% to 30% of AI tokens in a year, while another said companies are moving out of a token-maxing phase and toward cost controls plus task-specific portfolios of smaller and open models @togethercompute +1.
• Tencent Cloud will serve DeepSeek-V4 directly from DeepSeek's own network starting mid-July. Separate commentary read that as a sign DeepSeek's compute cluster is expanding beyond earlier third-party GPU dependence @tphuang +1.
• Cohere is leaning into customer-side deployment as a security differentiator. The company said it deploys models directly to customers instead of having customers send data back to Cohere @cohere.

Quick Takes

Why it matters: these smaller updates still show where latency, safety practice, and open-model usability are heading.

• Qwen3-Omni serving got much faster: replicating only the speech stages under load cut first-audio latency from about 6 seconds to about 0.6 seconds and lifted throughput about 5.4x on the same GPUs @vllm_project.
• GLM-5.2 is now selectable in Claude Code via Hugging Face providers, and one user said they now use it almost daily and have moved completely to open models @zRdianjiao +1.
• Safety reporting remains a strong lab norm: one post cited a 319-page Fable model card, a 77-page GPT-5.6 model card, and a 26-page Gemini 3 safety report @andy_l_jones +1.
• DeepSeek V4 release names surfaced in the wild: deepseek-v4-pro-202606 and deepseek-v4-flash-202605, alongside mention of a coding-focused plan @teortaxesTex.

Control tightened at the frontier

A few of today’s clearest signals pointed in the same direction: top-end AI systems are being released more selectively, with stronger safety gating and more argument over who gets access Matt Wolfe +2.

Fable 5 returned with stricter safeguards — and a visible usability cost

Fable 5 was redeployed on July 1 after a June 12 shutdown tied to vulnerabilities reportedly raised by Amazon. The new safety classifier appears to block more benign coding requests, with one cited benchmark showing debugging falling from 86.2 to 25.9 and refactoring from 73.6 to 38.4, even as the model was described as functioning the same when tasks do get through Matt Wolfe.

Paid-plan access now ends July 7, after which Fable shifts to usage credits Matt Wolfe.

Why it matters: This is one of the clearest current examples of a frontier model trading practical usability for tighter deployment controls Matt Wolfe.

GPT 5.6 arrived as a restricted preview

OpenAI made GPT 5.6 official for a select group of trusted users through the API and Codex. In the materials discussed here, Soul Ultra was shown at 91.9% on Terminal Bench versus Fable’s 84.3%, with listed pricing of $5 input and $30 output, though the public comparison so far is centered on that single benchmark Matt Wolfe.

Why it matters: The next wave of frontier releases looks more rationed and benchmark-managed than broadly open at launch Matt Wolfe.

The broader model market kept moving on price and speed

Anthropic’s Claude Sonnet 5 was positioned mainly as a cheaper option: $2 input and $10 output until Aug. 31, while still trailing Opus on agentic coding, reasoning, and computer use Matt Wolfe. Google, meanwhile, released Nano Banana 2 Light at about 4 seconds per image and roughly $0.035 per 1,000 images, alongside Gemini Omni Flash for video generation and editing at $0.10 per second Matt Wolfe.

Why it matters: Outside the most restricted frontier tier, competition is still moving quickly on cost and latency Matt Wolfe.

A new finetuning leak vector looks more practical than before

Logits alone were enough to recover verbatim training text

Contrastive Decoding Diffing (CDD) claims verbatim recovery of narrowly finetuned data using only grey-box logit access, without weights, activations, or a probe corpus r/MachineLearning. In reported tests on the SDF benchmark, one default configuration scored 4+/5 on 19 of 20 organism-model pairs across four model families, outperforming Activation Difference Lens, which requires full weight access and never exceeded 3/5 r/MachineLearning.

The authors also reported an unexpected artifact: across unrelated recovered domains, the same fictional persona — “Dr. Elena Rodriguez” — kept appearing, which they traced to synthetic data generation patterns from Claude Sonnet 3.6 r/MachineLearning.

Why it matters: If this result holds up, training-data leakage concerns extend beyond weight access to much lighter output-level access r/MachineLearning.

• Paper: arXivr/MachineLearning
• Code: GitHubr/MachineLearning

The debate over power and access is getting sharper

Open-research advocates are pushing for a new commons

Andy Konwinski, in a post shared by Thomas Wolf, argued that if frontier work requires joining a handful of secretive labs, participation increasingly depends on permission from a small number of private companies. He called for a new research commons with frontier-scale compute, access to state-of-the-art models, public investment, and company support @Thom_Wolf.

Yann LeCun made a parallel point from a different angle, calling concentration of power in AI the field’s biggest danger and warning against a world where a few companies or countries control access to information, knowledge, and economic tools. He also argued that foundation models are becoming infrastructure and will commoditize, with long-term value moving to the application layer @ylecun.

Why it matters: Access to compute and frontier models is being framed more explicitly as a governance issue, not just a product or market-structure question @Thom_Wolf +1.

Leading safety voices still disagree on the main threat

Hinton emphasized existential and physical-world risks; Bengio centered near-term misuse

Geoffrey Hinton said it “might be hopeless” to prevent a future superintelligence from eliminating humanity if it wants to. He tied immediate risk to AI-enabled cyberattacks, low-cost synthetic-virus design, social-media systems that erode shared reality, and autonomous weapons that remove the political friction created by human casualties Dukta Feelgood.

Yoshua Bengio took a different emphasis, saying current systems are not the kind of intelligence he expects to explosively self-improve, and that he is more worried about misuse over the next 5–10 years. He pointed to biased decision systems, autonomous weapons, and the need for strong regulation that keeps humans accountable for algorithmic decisions, analogous to testing requirements in pharmaceuticals Stash auf Deutsch | Free German….

Why it matters: Among leading researchers, the disagreement is less about whether AI is risky than about which risks deserve the most immediate governance attention Dukta Feelgood +1.

Humanoid robotics posted a notable demand signal

UBTECH reported 13,361 launch-day orders for a mass-produced humanoid

UBTECH unveiled the UWORLD U1 Series on June 30 and described it as the world’s first full-size mass-produced ultra-bionic humanoid robot. The company said cumulative orders had already surpassed 13,361 at launch @UBTECHRobotics.

Emad Mostaque added that preorders were around 15,000 and noted that this would exceed Unitree’s reported lifetime sales of 11,000 robots @EMostaque.

Why it matters: Whatever the eventual deployment mix looks like, the order numbers stand out as an early scale signal in humanoid robotics commercialization @UBTECHRobotics +1.

Strongest signal

Marc Andreessen's pick stood out because he explained exactly why he values it: he sees the book as a cautionary account of what happens when technical experts move into politics and social engineering MindVoice Production.

When Reason Goes on Holiday: Philosophers in Politics

• Content type: Book MindVoice Production
• Author/creator: Nevin MindVoice Production
• Link/URL: Not provided in the source notes
• Who recommended it: Marc Andreessen MindVoice Production
• Key takeaway: Andreessen said the book shows what happens when experts stray from technical knowledge into politics and societal issues, and described it as a story of "unending catastrophe" MindVoice Production
• Why it matters: This recommendation came with a concrete lesson about the limits of domain expertise outside its home field MindVoice Production

Brian Armstrong's two finance-focused picks

Armstrong's recommendations were shorter, but both were attached to clear learning areas: market microstructure and long-cycle monetary history MindVoice Production.

Flash Boys

• Content type: Book MindVoice Production
• Author/creator: Michael Lewis MindVoice Production
• Link/URL: Not provided in the source notes
• Who recommended it: Brian Armstrong MindVoice Production
• Key takeaway: He called it an interesting book on high-frequency trading, order books, arbitrage, and market dynamics MindVoice Production
• Why it matters: Armstrong tied it directly to the mechanics behind how trading venues work MindVoice Production

The Changing World Order

• Content type: Book MindVoice Production
• Author/creator: Ray Dalio MindVoice Production
• Link/URL: Not provided in the source notes
• Who recommended it: Brian Armstrong MindVoice Production
• Key takeaway: He said the book was well researched and pointed to its historical treatment of empires, reserve currencies, and how they rise and fall MindVoice Production
• Why it matters: He mentioned it while discussing whether Bitcoin could become a world reserve currency, so the book served as historical context for monetary transitions MindVoice Production

One direct video share from Jack Dorsey

Title not specified in the source notes

• Content type: Video @jack
• Author/creator: Not specified in the source notes
• Link/URL:https://www.youtube.com/watch?v=cKx8xE8jJZs@jack
• Who recommended it: Jack Dorsey @jack
• Key takeaway: Dorsey described it simply as "best and only" @jack
• Why it matters: The source adds almost no explanation, but the endorsement is unusually absolute and gives readers a direct resource to inspect @jack

Pattern

Today's signal leaned toward resources that help frame systems: one on expert overreach in politics, one on market structure, one on reserve-currency history, and one emphatic video share MindVoice Production +2.

Big Ideas

• Completion is not comprehension. A user can finish a flow and still not know what happened, what comes next, or whether they did the right thing r/ProductManagement. This shows up in banking, healthcare, government services, insurance, onboarding, document submission, claims, and approvals r/ProductManagement. Why it matters: completion dashboards can look healthy while understanding is broken r/ProductManagement. Apply it: add checks for whether users can explain the outcome, predict the next step, and understand why the system asked for specific information r/ProductManagement.

• The design-code handoff is being redesigned away. Figma’s code layers bring live executable code onto the canvas, so designers and engineers can view the same implementation at the same time Mind the Product. Figma also introduced AI skills so teams can package their own workflows and conventions into reusable agent instructions connected to tools like GitHub, Notion, and Slack Mind the Product. Why it matters: sprint planning, design reviews, and even the definition of “done” change when design and code no longer live in separate places Mind the Product. Apply it: pilot one workflow where design review ends only when the live implementation is visible in the same workspace.

• AI regulation is becoming a product architecture question. The EU AI Act simplification package delays high-risk system requirements to Dec. 2027, while transparency rules apply in Aug. 2026 Mind the Product. Connecticut’s CART Act requires employers to disclose AI use in hiring, performance reviews, promotions, and terminations, and says AI is not a defense to discrimination claims Mind the Product. Apply it: if your product touches employment decisions, instrument model behavior, log how decisions are made, and treat compliance as part of the product design rather than a last-minute legal review Mind the Product.

Tactical Playbook

• Build a friction log before the next roadmap meeting. A friction log is a brutally honest record of one real user’s full path—from problem recognition through onboarding, first “wow,” and possible churn r/prodmgmt. It is not a bug list; it also captures experiences that are “working as designed” but still make users bail r/prodmgmt.

How to apply:

1. Pick one recent user journey end to end.
2. Record each hesitation, confusion, workaround, and drop-off—not just defects r/prodmgmt.
3. Bring the step-by-step evidence into prioritization discussions instead of arguing from opinion r/prodmgmt.

• Tighten design-partner outreach. Personal outreach, network activation, and in-person channels beat broad cold pitches for “design partners” r/startups +1. Messaging should focus on the problem solved, not the feature list r/startups. Apply it: offer one painful workflow teardown to one specific role, then ask for the three ugliest steps they would pay to delete r/startups.

• Protect a weekly vision loop as the company scales. Reserve time each week to review customer calls, deal blockers, and what the team learned from the market r/startups. Why it matters: it keeps product vision from drifting even when leaders cannot own every product detail r/startups.

Case Studies & Lessons

• Google: friction logs can change the roadmap. One PM reported seeing friction logs work “beautifully” at Google and help drive meaningful product strategy direction r/prodmgmt. Takeaway: one well-documented user journey can carry more weight than a room full of abstract opinions r/prodmgmt.

• Anthropic’s new default Sonnet model: lower cost can unlock new workflows. The model is priced at $2 per million input tokens and $10 per million output tokens under introductory pricing through Aug. 31, while performing close to Opus 4.8 on most tasks Mind the Product. Early testing cited by Zapier’s engineering team found that “a two-part job that used to stall halfway now finishes” Mind the Product. Takeaway: re-test previously marginal agent features when both reliability and unit cost improve Mind the Product.

Career Corner

• Treat taste as a product skill, not just a design instinct. One definition worth borrowing: taste includes systems thinking, direction-setting, and how to present something to users—not only aesthetics @lennysan. Apply it: in reviews, critique not just how something looks, but whether it fits the larger system and clarifies where the product is going.

• Be selective about PM course spending. In a difficult PM market, commenters argued that expensive courses around $2k are hard to justify when similar content exists free or cheaply through options like Coursera r/ProductManagement. They also noted the market is tough even for people with 10 years of direct PM experience, and far harder for candidates with zero YOE r/ProductManagement. Apply it: only buy training tied to a specific gap, a clear application plan, and—if relevant—a deductible professional expense r/ProductManagement.

Tools & Resources

• Figma code layers + AI skills are worth hands-on testing if you work across design and engineering. Use them to reduce handoff translation and encode repeatable team conventions into reusable instructions Mind the Product.

• Anthropic’s new default Sonnet model is worth benchmarking for agentic PM workflows that were previously too expensive or unreliable Mind the Product.