Your intelligence agent for what matters

🔥 TOP SIGNAL

• The best coding-agent workflows are getting simpler up front and stricter underneath. Theo says his five-day Lakebed push ran mostly on GPT-5.5 with a handwritten Agent MD, 1-2 sentence prompts, fresh threads, and remote browser-based verification—instead of skill sprawl or rigid plan modes Theo - t3․gg. Jediah Katz makes the same point from benchmarks: simpler harnesses can generalize well, while benchmark-tuned setups can win by brute-forcing impractical tool use @KLieret +1. LangChain's Context Hub/Engine demos and Logan Kilpatrick's vibe-coding vs agentic-engineering split point to the same production standard: keep context explicit and versioned, then use traces, evals, checklists, and human review to control the system LangChain +2.

⚡ TRY THIS

• Write AGENTS.md like a letter, then prompt like a teammate. Handwrite the file yourself; describe what you're building, why it exists, and the general constraints/philosophy, but skip file paths and rigid technical mandates Theo - t3․gg. Then keep task prompts to 1-2 sentences, lead with the end goal, and when the model struggles, add a concrete example instead of more prose—Matthew Berman says the best coding-agent prompts are short, behavior-focused, and closer to this thing isn't working like I think it should ... go fix it than to a long interface spec Theo - t3․gg +1. Open a fresh thread for each concern so old context does not bias the next task Theo - t3․gg.

• Turn remote execution into a verification loop. Theo's setup: host T3 code remotely, connect it with Tailscale, and work from browser/phone/tablet so the agent keeps running after the laptop closes Theo - t3․gg. Then configure Codex computer use once in the app and call it through CLI/T3 code so the agent can deploy the app, open the live site, and verify behavior before it reports done Theo - t3․gg.

• Use traces to generate fixes and evals, not just dashboards. LangSmith Engine runs on a schedule against production traces, clusters issues into prioritized buckets, proposes prompt/agent-file/code fixes, opens PRs, and creates online evaluators plus offline regression examples from errored inputs LangChain. LangChain's main lesson from dogfooding: the hard part is filtering for meaningful issues, so store team priorities in an agent overview memory file—e.g. care about hallucinations, ignore latency, or the reverse—and let that steer what the system surfaces LangChain.

• Separate scratch space from durable memory. In Context Hub, create an agent repo with an AgentsMD contract, put long-lived guidance under /memories/..., and keep temporary thread-local files in a separate state backend LangChain. Let agents edit those memory files via normal file tools, keep the full version history visible to humans, and gate risky actions like Send Email behind an interrupt approval step LangChain.

📡 WHAT SHIPPED

• Antigravity 2.0: subagents, async task management, cron-style scheduled tasks, local shell-script JSON Hooks (docs), and voice input via real-time transcription @antigravity +5.
• Deep Agents v0.6: Delta channels cut checkpoint storage by up to 100x for long-running agents; LangChain shows 5.3GB falling to 129MB on a 200-turn coding session @LangChain.
• LangSmith Fleet: public beta for isolated execution and computer use. Agents can analyze data, transform files, generate/write code, and run shell commands in a secure virtual computer; LangChain says Fleet + sandboxes already power an internal docs agent that listens to requests, triages them, and opens PRs automatically @LangChain +1.
• Codex model cleanup: GPT-5.2 and GPT-5.3-Codex are leaving Codex for ChatGPT-account users on June 2; GPT-5.5 becomes the default frontier model on free plans, while the deprecated models stay available via API. Adoption signal: GPT-5.2 is now under 1% of production usage @thsottiaux +1.
• Private MCP tunnels for OpenAI products: teams can keep MCP servers inside their own network while Codex, ChatGPT, and the Responses API connect over outbound-only HTTPS. Docs: secure MCP tunnels@OpenAIDevs +1.
• Benchmark caution, not a product recommendation: Jediah Katz says mini-swe-agent—roughly 150 lines in its core agent class—beats ClaudeCode and Codex on DeepSWE, but he also warns benchmark-optimized harnesses can be impractical for normal development if they force excessive tool calls or sloppier code @KLieret +1.

🎬 GO DEEPER

• 27:14-28:15 — Theo on handwritten Agent MD. Worth watching if your agent instructions have turned into a brittle policy doc. His rule: no file paths, no rigid technical decisions—just a hand-written explanation of what you're building and why Theo - t3․gg.

• 39:47-40:45 — Theo on computer-use verification. Good clip if you want agents to validate real deployments instead of handing you untested diffs. He sets up Codex computer use once, then reuses it through T3 code for remote verification Theo - t3․gg.

• 15:43-17:39 — LangSmith Engine on eval design. Clear framing on why you need both targeted and end-to-end evals, why tool/context design still matters, and why prompt engineering is not dead inside production agent systems LangChain.

• Repo to study — email_agent Context Hub example.https://github.com/langchain-samples/deepagents-with-langsmith/tree/main/agents/email_agent is a clean reference for AgentsMD, shared /memories, and human-visible version history @LangChain +1.
• Repo/doc to study — SQLite AGENTS.md.https://github.com/sqlite/sqlite/blob/master/AGENTS.md shows a strict maintainer policy for agent-driven contributions: no agentic code, yes agentic bug reports with reproducible tests, and proof-of-concept patches only as documentation simonwillison.

Editorial take: the edge is moving away from clever prompt stacks and toward cleaner system design—short asks, explicit memory, real verification, and trace-backed eval loops.Theo - t3․gg +2

Funding & Deals

• Trajectory raised $15M from Conviction, Bessemer, Radical, Jeff Dean, Fei-Fei Li, and others. The company is building a continual-learning platform that uses product-usage signals to continuously post-train agentic models; its research team comes from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, and Scale AI, with product talent from Stripe and Figma, and it says partners including Clay, Harvey, Decagon, Mercor, and Rogo are already using the system, with some deployments in production @rronak_ +1

• TechCrunch’s Equity podcast surfaced Scrunch’s capital path: a $4M seed in March and a $15M Series A in July. The company pairs AI-search visibility analytics with an "agent experience" product that strips non-semantic page elements and serves bot-optimized versions at the edge TechCrunch

Emerging Teams

• InfinityOS / ProjectX_Cloud is a web-based OS that lets any device run Windows or Linux desktop apps, each with its own GPU but inside the same workspace and filesystem. YC’s demo claims included a Chromebook running Isaac Sim, an iPad rendering Blender in 4K, and a phone running 10 parallel agents; founders are Rounacc, Bishal, and runallapps @ycombinator

• KelAI is an autonomous research engine for hedge funds and institutional investors, built by an experienced quant PM, that runs idea generation, validation, monitoring, and feedback in one agentic system @ycombinator

• Apollo Atomics is building compact nuclear reactors with less than 24-month deployment timelines. Its wedge is a modified pressurized-water design that flips the steam generator to make the plant an order of magnitude smaller without reducing power; founders are Assil Halimi and Drew @ycombinator

• Rentahuman and Eden both point toward AI-native labor models in the physical world. Rentahuman lets AI agents communicate with and pay humans for real-world tasks and frames the mission around creating jobs and coordinating workers at global scale; Eden launched Eden I, an industrial semi-humanoid robot available for hourly hire @ycombinator +1

AI & Tech Breakthroughs

• Genesis World 1.0 is an open-sourced robotics simulation stack aimed at turning physical-world iteration into a compute problem. The release says one hour of real testing can become 100 days of simulation and describes a rebuilt stack including a GPU-accelerated cross-platform compiler, penetration-free multi-physics contact solvers, unified rigid and deformable physics, a photo-realistic Nyx renderer, and the Quadrants engine, which the team says delivers 10x faster launch and up to 4.6x runtime versus the prior Genesis release; it also reports near real-time dexterous manipulation across multiple embodiments with a lower sim-to-real gap @gs_ai_

• TritonMoE is a portable MoE inference kernel worth watching. A new preprint describes it as written entirely in OpenAI Triton for NVIDIA and AMD portability without vendor-specific code; the authors report a fused gate+up GEMM that eliminates 35% of global memory traffic, 89-131% of Megablocks throughput at inference batch sizes up to 512 tokens on A100, identical execution on MI300X, and limitations at 2048+ tokens or with 64+ experts under extreme routing skew r/MachineLearning

• Scrunch’s "agent experience" layer reframes technical SEO for LLMs. The company says key pages can be reduced by 98-99% in token count—for example from roughly 100k tokens to 1-2k—by stripping code, JavaScript, image tags, and other non-semantic content, then serving the optimized version only to bots while humans keep the normal page TechCrunch

Market Signals

• Startup formation and monetization look faster than the last SaaS cycle. A circulated Stripe datapoint set claimed new business creation was up 2x YoY in March, 20% of startups charged their first customer within 30 days versus 8% in 2020, time to $1M/$10M/$100M ARR is down about 2x, and average revenue per business is still up despite 2x more company creation @rexan_wong

• The app-layer thesis is shifting toward vertical scaffolding, not just better models. a16z argues that in complex verticals, value comes less from raw model capability than from the surrounding infrastructure that makes output trustworthy, compliant, and operational; horizontal categories like code generation improve directly with pre-training spend, while vertical problems require industry-specific systems @a16z

• Founder edge may be moving toward domain expertise and distribution. Another circulated take argued for vertical SaaS in under-softwared industries, said domain experts can outperform traditional Silicon Valley pedigrees, and pointed to a teacher-built MagicSchool AI as an example of how quickly AI-native companies can scale @rexan_wong

• AI search is becoming a distinct distribution surface. Scrunch says many customer sites now see more AI bot traffic than human traffic and that the gap is widening month over month; it also says AI referrals convert 400% higher than traditional organic search, while the broader backdrop is rapid growth in Google AI Overviews, AI Mode, Gemini, and longer open-ended queries TechCrunch

• Open source positioning remains a real wedge in AI dev tools. Pragmatic Engineer reports OpenCode rose from roughly 650k MAU to nearly 8M and almost 1M DAU in a few months, argues open source positioning was a major reason it captured the category, and also flags GPU demand as a system-wide bottleneck The Pragmatic Engineer

Worth Your Time

TechCrunch Equity on AI search — watch here. Useful background on how Google AI Overviews, AI Mode, and Gemini are changing discovery flows, plus a founder-level explanation of bot-optimized pages TechCrunch

Trajectory announcement thread — X post for the clearest primary source on the round, team pedigree, and early partner list @rronak_

OpenCode episode — Pragmatic Engineer if you want a tighter view on open-source positioning, GPU bottlenecks, and the shift toward AI coding agents The Pragmatic Engineer

TritonMoE — paper and code for a concise read on cross-platform MoE inference portability r/MachineLearning

InfinityOS and Apollo Atomics — InfinityOS launch and Apollo Atomics launch for quick primary-source product pages @ycombinator +1

Top Stories

Why it matters: today's clearest signals were where capital is concentrating, where frontier capability is accelerating, and where enterprise agents still fall short.

• Cognition raised at a new scale. The company said it raised over $1B at a $26B valuation, with enterprise usage up more than 10x since the start of the year and run-rate revenue at $492M. It also said Devin launched two years ago as the first AI software engineer, and that cloud agents have gone from niche to mainstream @cognition. The combination of financing, usage growth, and revenue scale makes this a major commercial signal for coding agents.
• GPT-5.5 made a large jump on offensive cyber tasks. Lyptus Research said GPT-5.5 now saturates its dataset, reaching a 5.1-hour time horizon at a 2M-token budget and solving 92.4% of tasks at 50M tokens, beyond 12 hours. The same benchmark line previously measured about 3 hours for Opus 4.6 at 2M tokens and described a doubling trend every six months since 2024; separately, a researcher said GPT-5.5 found a real 27-year-old RCE after checking the commit history @LyptusResearch +1. Capability gains are now showing up in both benchmark saturation and real bug-finding.
• Enterprise IT remains a hard benchmark. Artificial Analysis and IBM Research launched ITBench-AA for Kubernetes incident response and found every frontier model below 50% accuracy, led by Claude Opus 4.7 at 47% and GPT-5.5 at 46%. They also found that longer trajectories often hurt: GPT-5.5 averaged 31 turns per task at about 46%, while Gemini 3.1 Pro averaged 83 turns at 30% @ArtificialAnlys +1. Strong general-purpose models still need much better harnesses and workflows for enterprise ops.

Research & Innovation

Why it matters: the best research updates were about cutting training cost, improving self-improvement, and expanding AI into biology.

• Sakana AI's DiffusionBlocks reframes network training block by block. The method trains one block at a time, needs memory for only a single block, and matched end-to-end performance across ViT, DiT, masked diffusion, autoregressive transformers, and recurrent-depth transformers. For looped transformers, it can replace BPTT with a single forward pass during training @SakanaAILabs.
• Biohub released Evolutionary Scale Models. ESM is positioned as an open engine for protein prediction, design, and discovery, with a protein language model, ESMFold2, and an atlas containing 6.8 billion sequences and 1.1 billion predicted structures. The release says it has already designed cancer-related proteins and a PD-L1-binding antibody-like protein that worked in lab tests @TheTuringPost.
• Self-Verified Distillation offers a lighter path to improvement. The method lets an already post-trained reasoning model generate answers, verify them itself, and train only on responses that pass verification, without ground-truth answers or external verifiers @tonyh_lee +1.

Products & Launches

Why it matters: launches focused on practical retrieval, search, and document-processing tools rather than just bigger chatbots.

• Google DeepMind released Gemini Embedding 2. It is described as the company's first native multimodal embedding model, creating a unified representation for text, audio, video, and image inputs @mseyed.
• Surya OCR 2 raised the bar for open OCR. The 650M-parameter model scored 83.3% on the olmocr benchmark and 87% on an internal 91-language benchmark, with reported gains on tables, handwriting, forms, math, and layout. It runs on CPU, GPU, and MPS, with 5 pages per second on an RTX 5090 @VikParuchuri +2.
• Ask YouTube turns video search into a conversation. Google said the feature handles complex queries, supports follow-up questions, and returns structured responses built from relevant long-form videos and Shorts. It is live for Premium users in the U.S. and rolling out more broadly @Google.

Industry Moves

Why it matters: companies are now funding the layers above static models: continual learning, infrastructure, and social transition.

• Trajectory launched around continual learning. The startup says it uses product-usage signals to continuously post-train agentic models, has raised $15M, and is already working with companies including Clay, Harvey, Decagon, Mercor, and Rogo @rronak_.
• Modal raised a $355M Series C to expand its AI cloud infrastructure platform @StasBekman.
• OpenAI Foundation committed an initial $250M to measurement, transition support, and new approaches to broadly shared prosperity as AI reshapes work and the economy @sama +1.

Policy & Regulation

Why it matters: even without a major government ruling today, enterprise AI deployment is becoming more compliance-heavy.

• OpenAI added more governance controls for enterprise use. Its Admin API now supports spend alerts, model allowlists, data-retention controls, hosted tool controls, and more granular cost visibility; it also added Workload Identity Federation and support for private MCP servers over outbound-only HTTPS @OpenAIDevs +2.

Quick Takes

Why it matters: several smaller releases sharpened the picture on speed, cost, and agent infrastructure.

• Qwen3.5 on TokenSpeed hit 580 tokens per second for agentic workloads on NVIDIA GPUs @PyTorch.
• Perplexity open-sourced a Unigram tokenizer that cuts CPU utilization by 5-6x and runs in 63 microseconds at 514 tokens @perplexity_ai +1.
• Deep Agents v0.6 added Delta channels, cutting one 200-turn coding session's checkpoint storage from 5.3GB to 129MB @LangChain.
• Claude Code shipped reliability upgrades, including self-healing sessions plus MCP, streaming, and renderer fixes @ClaudeDevs +4.

Most compelling recommendation

Forbes 250: America's Greatest Innovators@vkhosla

• Content type: Article @vkhosla
• Author/creator: Alex Knapp @vkhosla
• Link/URL:https://www.forbes.com/sites/alexknapp/2026/02/11/forbes-250-americas-greatest-innovators/@vkhosla
• Who recommended it: Vinod Khosla @vkhosla
• Key takeaway: Khosla shared it as a list of the "all time 250 best innovators in the US" @vkhosla
• Why it matters: It gives readers one place to scan a broad set of U.S. innovators and decide where to dig deeper @vkhosla

Bottom line

If you want one link to bookmark from this set, save this one as a broad reference list for further exploration @vkhosla.

What stood out today

The clearest pattern was AI moving deeper into operating questions: financing at real scale, post-deployment learning, token economics, and system design for production agents @cognition +3.

The operating layer got clearer

Cognition pairs a huge round with rare operating metrics

Cognition said it raised over $1B at a $26B valuation led by Lux Capital, General Catalyst, and 8vc. It also said enterprise usage is up more than 10x since the start of the year and run-rate revenue has reached $492M; the company added that cloud agents have gone from niche to mainstream since Devin launched two years ago @cognition.

Why it matters: The announcement paired financing with concrete usage and revenue metrics, which gives a public read on enterprise demand for coding agents rather than just model hype @cognition.

Trajectory launches to turn product usage into continual learning

Trajectory launched as a research lab and product company building a platform for continual learning, saying it uses signal from product usage to continuously post-train large-scale agentic models. It launched with $15M in funding, named partners including Clay, Harvey, Decagon, Mercor, and Rogo, said some deployments are already in production, and described a team drawn from DeepMind, OpenAI, Apple, Meta, Amazon AGI, Scale AI, Stripe, and Figma @rronak_.

Why it matters: This is a direct bet that deployed products should keep improving after launch. Nathan Lambert argued continual learning is likely to show up first in knowledge-work products trained on real-world data and RL, while Sarah Guo framed the underlying problem simply: AI is still largely frozen after deployment @natolambert +1.

NVIDIA sharpens the AI factory pitch around token economics

NVIDIA is positioning AI factories as infrastructure for always-on reasoning models, agents, and multi-agent systems nvidia. It says Blackwell Ultra delivers the lowest cost per token, with GB300 NVL72 systems producing 50x more tokens per megawatt and 35x lower cost per token than Hopper, while Vera Rubin systems are designed for up to 35x higher performance per watt nvidia.

Why it matters: The story here is full-stack orchestration, not just chips: compute, memory, networking, software, and facility design tuned around token throughput and latency. NVIDIA is tying that pitch to deployment as well, naming Cisco, Dell, HPE, Lenovo, and Supermicro as partners and saying its own enterprise AI factory already uses hundreds of autonomous agents internally nvidia.

Research releases worth tracking

BioHub open-sources a new protein-model stack

BioHub released the MIT-licensed ESMC protein world model and ESMFold2, alongside an atlas of 6.8B proteins and 1.1B predicted structures. The project says ESMC was trained on 2.8B sequences, added metagenomic data beyond UniRef, and achieved state-of-the-art performance on protein interactions, especially antibodies, with evidence of inference-time scaling across five cancer and immunology targets Latent.Space +1.

Why it matters: The release is a strong argument for scaling large, general protein language models rather than relying only on specialized pipelines. Latent Space highlighted the claim that this approach can beat specialized systems like AlphaFold3 on some hard protein problems, especially where MSAs are weak or unavailable, such as antibodies Latent.Space +1.

Sakana's DiffusionBlocks aims at the training memory wall

Sakana AI Labs introduced DiffusionBlocks, a framework that trains networks one block at a time by interpreting each block as moving representations closer to the target, like a diffusion process. The approach only needs memory for a single block, matched end-to-end training across five architectures, and extends to recurrent-depth transformers by replacing backpropagation through time with a single forward pass during training @SakanaAILabs.

Why it matters: The paper is explicitly framed as a response to the resource wall created by end-to-end backprop. If the results hold up, it offers a practical route to reducing training memory without giving up competitive performance @hardmaru +1.

MiniMax's M2 report gives a rare look at production agent training

A new MiniMax M2 technical report argues that full attention still beat hybrid sliding-window variants for production use, and that linear or sparse attention was too fragile when KV-like state is stored in lower precision and when prefix caching matters for coding agents @RyanLeeMiniMax +1. It also describes an agent-training pipeline built from GitHub pull requests, runnable Docker environments, task-specific rewards, retained reasoning blocks across turns, and wall-clock rewards to discourage slow tool use; the report says self-evolution already handles 30-50% of daily RL iterations and improved internal evaluations by 30% @rasbt.

Why it matters: What stands out is the level of operational detail. The report discusses attention tradeoffs, agent harness design, and RL rewards rather than stopping at top-line benchmarks @rasbt.

Brief notes

• Genesis World 1.0: Genesis-Embodied-AI open-sourced a robotics simulation stack built around Genesis World, Quadrants, and Nyx, saying it reaches near-realtime performance, supports contact-rich dexterous manipulation, cuts launch time by 10x, and lowers the sim-to-real gap for zero-shot evaluation @gs_ai_.
• Private MCP inside enterprise networks: OpenAI introduced support for private MCP servers that stay inside a company's network while ChatGPT, Codex, and the Responses API connect through outbound-only HTTPS. The update is aimed at teams that want internal tool access without exposing the MCP server directly @OpenAIDevs +1.
• Pricing by outcomes, not tokens: Sierra said it is pricing AI agents on delivered outcomes such as a mortgage completed or a claim settled, rather than token usage. It is an early sign that some vendors want agent pricing tied to business results instead of raw consumption @SierraPlatform +2.

Big Ideas

• The bottleneck moved from shipping to choosing and scaling. AI has removed engineering bandwidth as the default excuse, but GoDaddy argues the real constraints are now scale, GTM, integration, and monetization Product School. Miro and Brian Balfour add that faster individual output creates more divergence, while onboarding and distribution still move at human speed Product School +1. Apply it: spend more time on selection, rollout, and adoption metrics than on idea-to-prototype speed.
• Customer-facing AI is system design, not prompt design. Amazon Games' framework: define identity, context, judgment, and interaction around the model Product School. Aakash Gupta's maturity model says the goal is AI that sees what the user sees, instead of making them restate context Aakash Gupta.

• Shared context is becoming core product infrastructure. Product School frames AI-native teams as a combination of system (shared workspace, agents, integrations, guardrails) and people (org design, training, incentives) Product School. Asana's work graph and Miro's canvas are both attempts to give humans and agents a common layer to act on Product School +1. Apply it: reduce tool fragmentation before buying more copilots.

Tactical Playbook

• Prioritize against one outcome, not generic impact.
  1. Declare the optimization goal.
  2. Score all work against it.
  3. Make trade-offs explicit.
  4. Frame deferrals as a "delayed yes" with artifacts ready to restart.
     One PM used renewal-influenced revenue to choose among four initiatives, protecting near-term revenue while deferring a larger long-term bet r/ProductManagement. Pair that with a fixed investment window so you do not re-litigate strategy at the first sign of friction Run the Business.
• Stop meeting spirals in tool decisions. Set success criteria early, separate input providers from decision-makers, identify the main persona, and run a small pilot with clear exit criteria r/ProductManagement +1. Why it matters: without this, every stakeholder judges through a different lens.
• Replace handoffs with prototypes and internal usage gates. Business Insider is bringing engineers in earlier and using more "show, don't tell" prototyping Product School. Anthropic ships internally first and uses internal adoption before external release Product School. Apply it: prototype before full consensus, but gate launches on real use.

Case Studies & Lessons

• Claude Code: tiny pods + dogfooding + live-code quality checks. Claude Code started as an internal side project, spent about three months iterating internally, and launched externally after internal adoption passed a DAU threshold Product School. The team works in 3-5 person pods with fluid roles and evaluates quality in live code rather than in PRDs or mocks Product School. Result cited: $2.5B first-year revenue and 51% of the coding marketProduct School. Takeaway: internal usage can be a better launch gate than polish.
• Business Insider: engineering outran product. Teams completed estimated 3-4 month efforts in weeks and started asking for more work Product School. That pushed product to reduce gatekeeping, blur product/engineering roles, and shift prioritization toward "is this a good idea?" while watching for product bloat Product School. Takeaway: when capacity expands, portfolio discipline matters more than backlog volume.

Career Corner

• PM value is moving from 0-100 to 80-100. GoDaddy argues LLMs now provide much of the first 80%, so PMs add value through context, evals, prompts, and gap-filling Product School. Brian Balfour makes the organizational version of the same point: mature teams need curation, not just prioritization, because they can build more than they should ship Miro. Career move: get better at judgment, customer intelligence, and end-to-end experience ownership.
• Build AI fluency socially, but review outputs ruthlessly. Fivetran saw adoption accelerate through hackathons, AI spotlights, tool access, and peer sharing Product School. At the same time, one PM described AI-written specs as polished but inaccurate inputs to engineering r/ProductManagement. Career move: show your AI workflows, but never outsource understanding.

Tools & Resources

• Capture-to-prototype builders. Alloy's workflow starts with capturing a live screen, generating a brand-aligned prototype in minutes, then connecting the codebase and tools like Slack/Jira so AI can build reviewable features in context Product School.
• Reusable AI skills. Slack's "skills" package recurring workflows like daily briefs or incident summaries into reusable instructions, reducing ad hoc prompting and making delegation easier Product School.
• Shared alignment layers. Miro's canvas model pulls inputs from tools like Slack and Confluence into one space, then turns messy feedback into summaries, QA criteria, and implementation handoffs Product School. Good fit when alignment—not ideation—is the bottleneck.