@AWS architect. Originally Microsoft C#.NET lover. Agile worshipper. {Stateless in a stateful Serverless world}

Boston
Joined October 2009
pages.awscloud.com/GLOBAL_AW… Compare approaches across architecture, deployment, performance, security, cost Tech & implementation decisions with evaluation criteria + best practices Discover solutions in AWS Marketplace that help you build, deploy, and govern data and AI workloads
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What if an AI bubble bursts — and that accelerates AI job losses? : Planet Money : NPR 1978, economist Charles Kindleberger book titled Manias, Panics, and Crashes: A History of Financial Crises. Drawing on the theoretical work of economist Hyman Minsky share.google/SeA8t1PYuN3i2ZZ…
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Bala Subra retweeted
Steven Sinofsky on why it's so hard to diffuse AI into companies: most work runs on know-how that's hard to turn into step-by-step instructions for an agent. "Algorithmic thinking is really, really, really hard for the vast majority of people who have jobs." "If you were to go to any person and ask them to create a flow chart for a particular thing that they have to go do, they would probably fail at producing that flow chart." "Say doing a marketing plan and there's 50 marketing people working on a giant product line. One person probably understands and could document the flow chart." "So if you put one of these agents in front of people to create these things, their ability to explain to it what to do is really, really limited." "You're basically just developing the next abstraction layer for how people interact. And developing an abstraction layer has historically, at each level of the abstraction layer, been a highly skilled, very specific individual within an organization." @stevesi
Jean-Denis Greze, founder of @TownAI, argues AI isn't diffusing because most people aren't tinkerers: a text box isn't enough, so the product must both identify the value prop and perform the work for them. "There's this business that rents surfboards on an island to people and then uses Town to inspect surfboards and little beach buggies, whether they've been damaged, to decide whether afterwards the person should get their deposit back." "What makes it work there is the tinkerer... The unlock is, is there someone over there that gets AI enough to wanna tinker a solution to their problem?" "And the problem with that is that there aren't enough tinkerers in the world, or there aren't enough people that have the time to be tinkerers to help their business. And I think that's why AI is not diffusing." "Instead what you're doing is you're using [AI tools] as a replacement, as like a better Google search." "If you can make a non-tinkerer into getting the same value as a tinkerer... you could be a ginormous business today because AI can solve so many business problems. You don't need more intelligence. The intelligence is there." @jgreze @arampell
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As an AI Agent Engineer, you must build these projects. 1.) Durable State Machine Workflow Build: A Temporal or LangGraph DAG with checkpoints, exponential retries and crash recovery for a 4-hour data migration task. Why: `while True` chat loops die on server restarts. Real-world workflows require deterministic state management. 2.) Raw MCP Server & Client Build: A JSON-RPC Model Context Protocol server exposing a local Postgres DB with scoped read/write permissions, written without a wrapper SDK. Why: Agents need standardized, secure hands. Bespoke API wrappers do not scale across an enterprise. 3.) Computer-Use GUI Agent Build: A vision-based agent using Playwright and DOM parsing that logs into a legacy web portal, navigates menus via screenshots and downloads a CSV. Why: 80% of enterprise software has no API. GUI agents unlock the legacy world text-based agents cannot touch. 4.) 3-Tier Memory & Eviction Engine Build: A memory system with working scratchpads, episodic session compression and semantic vector search with automated TTL eviction. Why: Infinite context causes OOM errors and hallucinations. Memory must be actively managed, scored and pruned. 5.) Multi-Agent Swarm with Typed A2A Build: A 3-agent swarm (Researcher, Coder, Reviewer) that passes strictly typed Pydantic state objects, never raw text. Why: Single agents fail on complex tasks. Swarms divide and conquer but raw text handoffs cause context drift. 6.) Sandboxed Code Executor Build: A code-interpreter agent that writes and executes Python in an isolated E2B or gVisor microVM with zero internet egress. Why: Letting an LLM run `os.system()` on your host machine is a catastrophic security risk. 7.) Inter-Agent Sanitization Proxy Build: Middleware that intercepts messages between agents, stripping malicious instructions to prevent cross-agent prompt injection. Why: Multi-agent systems create internal attack surfaces. Agent A can be tricked into hijacking Agent B's tools. 8.) Async HITL Approval Gate Build: A workflow that drafts a $10k invoice, serializes its state and waits 3 days for a human to click "Approve" in Slack before resuming. Why: High-stakes automation requires human accountability. Agents must know how to pause, wait, and resume cleanly. 9.) Self-Healing Coder Loop Build: An agent that reads its own stack traces, parses the compiler error, rewrites the failing code, and retries up to 3 times. Why: Agents that fail on the first try are useless. Agents that debug themselves are digital employees. 10.) Trajectory CI/CD Gate Build: A GitHub Action that replays 100 agent trajectories and blocks the PR if the tool-call sequence deviates from the golden DAG. Why: Grading the final text output hides the fact that the agent hallucinated an API call to get there. 11.) API-to-Browser Fallback Router Build: A routing layer where if the primary REST API 404s or times out, the agent seamlessly spawns a headless browser to finish the job. Why: The real world is messy. Agents must adapt to broken environments without crashing the workflow. 12.) Agentic Circuit Breaker Build: A middleware tracking loop depth and tool-call recursion that instantly halts the agent if it enters an infinite spiral. Why: A runaway agent loop is an outage with an invoice. It will burn $500 in API credits before you notice. 13.) Dynamic Context Budget Orchestrator Build: A pre-flight assembler that strictly allocates tokens across system prompts, retrieved docs and tool schemas per request. Why: Context overflow is the #1 silent killer of production agents. 14.) Shadow Trajectory Comparator Build: A silent 5% traffic mirror that runs a new agent topology in the background, diffing tool-call paths and costs without affecting users. Why: You cannot A/B test non-determinism safely in production without shadow mode. 15.) Public Architecture Teardown Build: 3 published deep-dives detailing your swarm topology, state-machine design, inter-agent security and cost-per-task analysis. Why: Top AI labs hire Agent Engineers based on their system design judgment not their ability to import LangChain. Systems that prove you can orchestrate, secure & scale when autonomous agents touch your stack. Most people watch tutorials. Builders ship systems. Bookmark & Repost.
If you learn AI Agent Engineering, you will never be unemployed again. I’d do this. Stage 1: Agent Architecture & State Machines - Learn: Directed Acyclic Graphs (DAGs), cyclic workflows, LangGraph, Temporal dot io, state serialization. - Practice: Build a multi-step agent using a state machine that can loop, branch and pause without using a fragile `while True` loop. - Why: Chat loops crash in production. Real-world workflows require deterministic state management and crash recovery. Stage 2: Tool Use & MCP (Model Context Protocol) - Learn: JSON-RPC, strict Pydantic schemas, MCP server/client architecture, dynamic tool discovery. - Practice: Write a raw MCP server from scratch exposing a local Postgres DB, handling auth and scoped permissions without using a wrapper SDK. - Why: Agents need standardized, secure hands to do real work. Bespoke API wrappers do not scale across an enterprise. Stage 3: Computer Use & GUI Automation - Learn: DOM parsing, Set-of-Mark prompting, coordinate mapping, screenshot diffing, Playwright/Puppeteer. - Practice: Build a vision-based agent that logs into a legacy web portal, navigates menus via screenshots and downloads a CSV. - Why: 80% of enterprise software has no API. GUI agents unlock the legacy world that text-based agents cannot touch. Stage 4: Advanced Memory & Context Engineering - Learn: 3-tier memory (working, episodic, semantic), vector eviction policies, context compression, token budgeting. - Practice: Build a memory engine that automatically compresses a 50-turn conversation into an episodic summary to prevent context overflow. - Why: Infinite context causes hallucinations and OOM errors. Memory must be actively managed, scored, and evicted. Stage 5: Multi-Agent Swarms & A2A Handoffs - Learn: Agent-to-Agent (A2A) protocols, typed state passing, supervisor vs. peer-to-peer topologies, consensus voting. - Practice: Build a 3-agent swarm (Researcher, Coder, Reviewer) that passes strictly typed JSON state, never raw text. - Why: Single agents fail on complex tasks. Swarms divide and conquer, but raw text handoffs cause context drift and hallucinations. Stage 6: Sandboxed Execution Environments - Learn: E2B, WebContainers, gVisor, Docker-in-Docker, resource limits, network egress blocking. - Practice: Build a code-interpreter agent that writes and executes Python in an isolated microVM with zero internet access. - Why: Letting an LLM run `os.system()` on your host machine or production server is a catastrophic security risk. Stage 7: Inter-Agent Security & Guardrails - Learn: Indirect prompt injection, tool poisoning, sanitization proxies, least-privilege scoping. - Practice: Build a middleware that strips malicious instructions from an email before the "Email Reader" agent passes it to the "Database" agent. - Why: Multi-agent systems create internal attack surfaces. Agent A can be tricked into hijacking Agent B's tools. Stage 8: Human-in-the-Loop (HITL) & Breakpoints - Learn: Asynchronous approvals, webhook routing, Slack/Teams integrations, durable execution pauses. - Practice: Build an agent that drafts a $10k invoice, serializes its state, and waits 3 days for a human to click "Approve" in Slack before resuming. - Why: High-stakes automation requires human accountability. Agents must know how to pause, wait and resume cleanly. Stage 9: Self-Correction & Reflection Loops - Learn: Critic agents, traceback analysis, retry with context, compiler error parsing. - Practice: Build a coding agent that reads its own stack traces, rewrites the failing code and retries up to 3 times before escalating to a human. - Why: Agents that fail on the first try are useless. Agents that debug themselves are digital employees. Stage 10: Trajectory Evals & CI Gates - Learn: Step-level grading, golden DAGs, trajectory distance metrics, LLM-as-a-judge for tool calls. - Practice: Build a CI pipeline that replays 100 agent trajectories and blocks the PR if the tool-call sequence deviates from the golden path. - Why: Grading the final text output hides the fact that the agent hallucinated an API call or skipped a safety check to get there. Stage 11: Graceful Degradation & Fallbacks - Learn: Circuit breakers, API-to-browser fallbacks, model cascading, cost-kill switches. - Practice: Build a routing layer where if the primary REST API 404s, the agent seamlessly spawns a headless browser to finish the job. - Why: The real world is messy. Agents must adapt to broken environments and degraded APIs without crashing the workflow. Stage 12: Public Agent Teardowns & Portfolios - Learn: Architecture decision records (ADRs), cost-per-task analysis, latency profiling, public benchmarking. - Practice: Publish a teardown of your multi-agent swarm, detailing the exact cost, failure rate, state-machine design and eval methodology. - Why: Top AI labs hire Agent Engineers based on their system design judgment and production reality not their ability to import LangChain. Bookmark and Repost!
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Agentic Monitoring Teams/fleets/swarms of agents are being used for offensive cyberattacks, and defenders are beginning to build up similar defensive capabilities (e.g. OpenAI’s Daybreak Blue, Anthropic’s Project Glasswing, Google’s Fairwind). benjamincongdon.me/blog/2026…
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JetBrains Mellum 2.1: 47% SWE-Bench, Apache 2.0 [2026] Mellum 2.1, the follow-up to last year’s Mellum2, jumped from a 2.0 score on SWE-bench Verified to 47.0 without changing its underlying architecture at all. share.google/ALxrlZkgCyJiDf1…
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Bala Subra retweeted
The Top AI Papers of the Week (October 12 - 18): - Harness^2 - ReHarness - LongHarness - The Agentic Scientist - Hybrid Reasoning Agents - Quantum Circuit Discovery - Scaling Discovery Requires Forgetting Read on for more:
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AWS Well-Architected Agent - correlates resource configurations, utilization metrics, app topology, declared business goals to prioritize recos at the resource, application, architecture levels, including trade-off analysis + can review Terraform IaC share.google/U6zC2mCELGqGLEm…
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Reverse Engineer Anything Project Brings AI Agents to Binary Analysis - InfoQ REA is an open-source project with over 200K GitHub stars that connects coding agents such as Claude Code and Codex to reverse-engineering tools like Ghidra, Hopper, and IDA share.google/38f95QbA9mn8lvH…
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technologyreview.com/2026/10… A new AI tool can guess what you’re looking at just by analyzing your brain scans—and re-create that image with remarkable precision. It can go the other way, too, and predict a person’s brain activity based on what they’re looking at.
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If you learn Forward Deployed Engineering, you will never be unemployed again. I’d do this. Stage 1: Enterprise Identity & Access (The Gatekeeper) - Learn: OAuth2, SAML, OIDC, SCIM provisioning, Row-Level Security (RLS) and multi-tenant data isolation. - Practice: Build an auth bridge connecting your AI app to a simulated customer's Okta/Azure AD, ensuring User A can only query data their specific RBAC role permits. - Why: No SSO and strict data isolation means no enterprise contract. Security review is the first gate. Stage 2: Air-Gapped & VPC Deployments (The Environment) - Learn: Kubernetes (K8s), Helm, Terraform, private container registries, air-gapped networking and egress filtering. - Practice: Script a one-click Terraform/Helm deployment of your entire AI stack into a simulated customer VPC with zero egress to the public internet. - Why: Banks, hospitals and governments don't let your AI talk to the public cloud. You must deploy in their basement. Stage 3: Legacy Data Extraction & ETL (The Fuel) - Learn: OCR, unstructured PDF parsing, mainframe DB connectors, Apache Airflow, dbt and data reconciliation. - Practice: Build a pipeline that ingests 10 years of messy, scanned customer invoices and turns them into a clean, queryable hybrid (vector + relational) store. - Why: Enterprise AI starves without clean data. The customer's data will always be a mess. Stage 4: Secure RAG & Permission-Filtered Retrieval (The Brain) - Learn: Hybrid search, document-level access control, citation grounding, metadata tagging and abstention logic. - Practice: Build a RAG pipeline where the LLM dynamically filters out retrieved chunks that the specific user asking the question doesn't have clearance to see. - Why: An AI that leaks HR salaries or M&A docs to a junior employee is an immediate breach of contract. Stage 5: MCP & Legacy System Integration (The Hands) - Learn: Model Context Protocol (MCP), SOAP/REST translation, web scraping, headless browsers and API rate-limit handling. - Practice: Build an MCP server that lets an LLM safely query and write to the customer's 15-year-old on-prem SAP or Oracle instance without exposing raw credentials. - Why: Agents are useless if they can't touch the customer's actual systems of record. Stage 6: AI Guardrails & PII Redaction (The Shield) - Learn: NER (Named Entity Recognition), deterministic regex filtering, LLM output validation, UUID masking, and audit logging. - Practice: Build a middleware proxy that intercepts LLM inputs/outputs, masks SSNs and credit cards with UUIDs before they hit the model, and restores them post-generation. - Why: Compliance teams will kill the deal if a single PII token hits an external frontier API. Stage 7: Human-in-the-Loop (HITL) Workflows (The Trust) - Learn: Temporal {dot} io, webhook routing, state machines, durable execution and UI approval gates. - Practice: Build a workflow where the AI drafts a $50k purchase order, but physically pauses execution until a human manager clicks "Approve" in Slack or Teams. - Why: Enterprises adopt AI only when humans remain legally accountable for the final action. Stage 8: Edge & On-Prem Inference (The Silicon) - Learn: vLLM, TensorRT-LLM, Ollama, GPU partitioning (MIG), and local quantized models (AWQ/GGUF). - Practice: Deploy a quantized 8B model on a single customer-owned GPU node to handle highly sensitive triage locally without network latency. - Why: Sometimes the data is too hot to move. You must bring the model to the data. Stage 9: Observability & Customer Health (The Pulse) - Learn: OpenTelemetry, Langfuse, custom Grafana dashboards, usage telemetry and cost-attribution. - Practice: Build a "Customer Health" dashboard showing the exact cost-savings, time-saved and token-spend the AI has generated for their specific team this month. - Why: If the customer doesn't see the ROI quantified, they will churn at renewal. Stage 10: Evals on Customer Data (The Proof) - Learn: Golden dataset creation, trajectory grading, shadow testing, and LLM-as-a-judge calibration. - Practice: Take 100 real past customer support tickets, run them through your agent in shadow mode, and prove to the VP of Support that it resolved 80% accurately. - Why: Generic benchmarks mean nothing to a buyer. You must prove the AI works on *their* messy data. Stage 11: Technical Discovery & Scoping (The Translation) - Learn: Stakeholder interviewing, architecture diagramming, SOW (Statement of Work) writing and expectation management. - Practice: Translate a vague "we want AI for supply chain" executive request into a scoped, 4-week technical deployment plan with defined, measurable success metrics. - Why: FDEs are the translators between revenue (Sales) and reality (Engineering). Stage 12: The Handoff & Self-Serve Admin (The Scale) - Learn: Technical writing, runbooks, admin portals, automated onboarding and chaos testing. - Practice: Write a deployment runbook and build an admin UI so the customer's internal IT team can manage the AI stack, update prompts and view logs without calling you. - Why: Your leverage is measured by how little the customer needs you for day-to-day operations. AI labs build the engine. FDEs build the roads. The modern Forward Deployed Engineer builds the secure, durable nervous system that connects frontier intelligence to legacy reality. Bookmark and Repost!
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Open-source projects for AI agent development: Strands Decider 2B, a small model designed to make decisions within agent workflows Strands Box, a sandbox for controlling agent execution. Both projects address different aspects of building agents share.google/yXxk7wORsChQdfl…
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While sandboxes are a commodity, serving millions of them under production-grade traffic requires a lot of detail to scale agents for businesses and consumers. We're open-sourcing the sandbox platform SPACE that powers all of Perplexity Computer to support the ecosystem. github.com/perplexityai/spac…
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Bala Subra retweeted
Six Thinking Hats came out 40 years ago, and plenty of companies still run meetings with it. I used Opus 5.5 to turn it into 𝟲 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝘀𝘂𝗯𝗮𝗴𝗲𝗻𝘁𝘀, basically my own team of advisors. Edward de Bono once wrote, "The main difficulty of thinking is confusion. We try to do too much at once. Emotions, information, logic, hope, and creativity all crowd in on us. It is like juggling with too many balls." His fix was six hats in six colors. Wear the white hat and you only talk facts. Wear the black hat and you only look for risks. ⚪ white-hat (medium): the facts ↓ same facts to all four, at the same time 🔴 red-hat (low): gut call 🟡 yellow-hat (medium): the case for it ⚫ black-hat (high): risks 🟢 green-hat (high): options, tried in its own worktree ↓ all five outputs 🔵 blue-hat (xhigh): one plan To set up the hats, send this straight to Claude Code 👇 "Set up Edward de Bono's Six Thinking Hats as six subagents in .claude/agents/. Create them, then stop. Don't use them yet. - white-hat (effort: medium, read-only): facts only. List what we know and what's missing, and mark each point verified or guessed. - red-hat (effort: low, read-only): gut reactions in a few lines. No justification needed. - yellow-hat (effort: medium, read-only): the strongest case for doing it and the value it creates. - black-hat (effort: high, read-only): risks and what could break. Point them out, don't fix anything. - green-hat (effort: high, isolation: worktree): alternatives and new options. It may try them in its own worktree. Keep scratch files inside it, and end by listing the files it changed and where the worktree is. - blue-hat (effort: xhigh, read-only): reads everyone's output and returns one plan: the decision, next steps, open questions. Give each hat its matching color where Claude Code has one. If this project had no .claude/agents/ folder before, remind me to restart Claude Code when you're done." To use them 👇 "Use the six hat subagents in .claude/agents/ on the decision below. Call each hat by its agent type. If a type isn't available, stop and tell me to restart Claude Code. Don't fall back to general-purpose. Run white-hat first. Then run red-hat, yellow-hat, black-hat and green-hat in parallel, each with white-hat's facts. Last, give blue-hat the full output from all five. Show me the plan before changing anything on the main branch. Decision: [one sentence on what you're stuck on]" Once they're split into subagents, four hats think at the same time. A full round on Opus 5.5 took 𝟴.𝟱 𝗺𝗶𝗻𝘂𝘁𝗲𝘀. Really efficient.
𝘀𝘁𝗼𝗽 telling Claude Code/Codex "no em dashes". 𝘀𝘁𝗼𝗽 telling Claude Code/Codex "stop saying delve". 𝘀𝘁𝗼𝗽 telling Claude Code/Codex "don't sound like AI". you never gave it a 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺. every README, PR description and landing page ships in the same AI voice, and you ban words one at a time. orwell wrote the fix 𝟴𝟬 𝘆𝗲𝗮𝗿𝘀 𝗮𝗴𝗼. six rules, 1946. paste them into your global CLAUDE.md / AGENTS.md and every session picks them up: → never use a long word where a short one will do → if it is possible to cut a word out, cut it out → never use the passive where you can use the active 6 blocks you can copy-paste directly 👇
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My 50 tips for using AI: 1. Data, data, data. Feed your AI as much good quality data as possible to fill in the context it hasn't got between its training data and what you want from it. With no context you'll get very generic responses. 2. Use chat history and chat memory. Build this up over time. Brain dump on it regularly. Have dedicated sessions to query the memory, fix it, and add to it. 3. Add custom instructions to fine-tune how you want it to respond and act, including the way it writes. 4. Clean up your local workspace. Add readmes to each folder to explain its context. Don't include obvious things any AI would be able to figure out. 5. Add an agents .md file to the root of your workspace. Again include context gaps only or regularly occurring mistakes. Keep it short. Use it like an if-then-else and point to an agent-docs folder with more instructions and context. 6. Use some form of task management tool, like Linear. Built-in tools are still pretty awful from my experience. Make sure to keep your task (issues) in a project. For more granular steps include checklists in your issues. 7. Use voice. Unless you're the fastest typer in the world, voice is always going to be quicker. It also lets you speak your mind. ChatGPT has great dictation, others are OK, but Copilot's particularly awful for some reason. 8. Use personal AI assistants or one session that can control all other sessions. The former is still early but is the end game for AI, get used to them. ChatGPT is really the only one that can do the latter properly at the moment. 9. When using your voice use a condenser mic that lets you talk incredibly quietly, thus not disturbing the people around you. 10. Give your AI various things, such as a master plan, principles, and an identity. 11. Ask your AI to keep a changelog and audit. 12. For schedules plan a way to have a backup so if you run out of tokens another AI can pick them up. 13. Use your AI on your mobile. It's surprising how much you can actually do. I can see a lot of us will go down to fewer and fewer and smaller and smaller screens. 14. Connect any and all relevant apps. I recommend GitHub for code work, Linear for tasks & project management, Notion for wiki and KB, Google Drive for storage and workspaces, Neon for the database, and perhaps something for video generation + image generation for Claude. 15. Use GitHub for coding. No you don't need to do any coding yourself. All control is managed by the AI. 16. And just don't be afraid of coding or building apps. You now have the power to build any app and tool you wish. There is no need to use proprietary interfaces anymore if you don't have to. 17. Ask your AI to set a schedule to review and research updates on all the tools and systems you use in your workspace. Ask it to find best practises, model releases, system card, updates, and prompting guides and ask it to suggest changes and improvements in your workspace after its review. 18. Stop doing admin. Give it to your AI. The only thing I would be wary of is managing finances. Though if you're in an area of the world that has a finance plugin such as ChatGPT in the US, use that. Other than that, from my experience, an AI can do everything else. 19. Markdown files are good but I'd recommend HTML files for explaining stuff. It's just better on the eye and gives richer content 20. Stop reading and using influencers prompts and guides (not including this one, of course 😜). They have no idea about you. You need to figure that out. 21. If you have any expertise, you are still better at that than people without that expertise so take advantage of that. AI does not make you an expert of something you are not, at least not yet or not instantly. 22. Though I'd argue coding and data analytics are solved, potentially a few other areas as well. 23. Stop paying for third-party tools and systems when AI can do that job. Ask your AI first. 24. Give it small things to do at first, then build up and up. Ask it to verify its output and check evidence. Ask it to ask you questions. 25. Give it examples and counterexamples; screenshots, files, and photos. If you're designing websites or tools, produce mockups with a relevant image generator. 26. There's no real need to start a new session nowadays but certainly create new sessions for new projects and put the AI in a new folder with project specific instructions so that session solely works on that. Use it like a worker. Keep it focused. 27. Never rely on a single model. You should have at least two chatbot plans. Use for second opinions and a backup for when one has an outage. Or just to run more work. 28. Document things you want the AI to permanently remember. Don't rely on memory and history for that. 29. Ask your agent to check your knowledge base and workspace for any conflicting info and work with it to fix it. 30. Make sure your knowledge base is easy to search, including the file names, with little explanation. 31. Build systems that last. If you're running repeatable tasks consider building a skill. I like to call these specialists. 32. Keep files and workspaces fresh. Remove any stale data. 33. Explain what "finished" and "complete" mean. 34. Prepare for mistakes. Always back up or have a way to recover. 35. AI gives you time to prototype over and over before you find your final solution. Long gone are the days of risking time on one idea for weeks or months. You can now test multiple ideas that only waste hours or days. You only need one of these to work. 36. When working with agents for the first time, start with all restrictions on and slowly but surely give it more and more freedom. They work better with fewer restrictions. 37. Only use AI if you need intelligence. Else use automation. Remember AI is still not 100% accurate, or 100% hallucination-free. Nor is it cheap. Tokens are precious. 38. So ask the AI to use scripts, formulas, and schedules where it can. 39. Schedules allow you to run your AI while you're not even at your desk, such as overnight, at weekends, and on all holidays. Set exactly what you want it to do: some AI can set fairly dynamic schedules (such as one piece of work during the week and a different piece of work during the weekend) or are reactive depending on the results 40. Ask your AI to run parallel sessions to get more work done quicker. Or if you're not using a personal AI assistant or orchestration, run multiple sessions at once yourself. 41. Use desktop apps for your AI. They give you so much more than the web versions. 42. Speak to your AI a lot. Most AI are powerful enough out of the box that a lot of prompting techniques are now dead. Talk to it like you talk to a person to get the best out of it. 43. AI is like a work colleague, be it one without context. Treat it as such. 44. Get it to question your assumptions, identify weaknesses, and present alternatives. Ask for pushback 45. Always fact-check important claims and ask for sources and dates. 46. Check what it can actually access. Don't presume what it tells you is correct. 47. No need to wait for the perfect use case. Just give it a problem right in front of you now. 48. Watch for silent failures such as schedules breaking. 49. This advice will get outdated very quickly. Always keep up to date with the latest news and techniques. 50. My top tip is just to start using it. The earlier the better. You'll start to understand how they work and their pros and cons. You'll also start to build up context, memories, and history, which are hugely vital. We're at the stage where we are now delegators and supervisors, get the AI to do most of the work as long as you give it good instructions at the start, check it thoroughly at the end and guide it in the middle.
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Bala Subra retweeted
wow.. Anthropic and Google now teach Claude Code to design 15 github repos that turn Opus 5.5 into a full design team: GIVE CLAUDE TASTE 01. frontend-design by Anthropic: github.com/anthropics/skills -> Anthropic's own skill for distinctive, intentional UI with a real point of view 02. stitch-skills by Google: github.com/google-labs-code/… -> generate screens, turn code back into designs, keep one DESIGN.md system 03. ui-ux-pro-max-skill: github.com/nextlevelbuilder/… -> 192 color palettes and 74 font pairings your agent can search 04. impeccable: github.com/pbakaus/impeccabl… -> 24 commands like polish, critique and bolder, plus 59 rules that catch AI-looking UI 05. agent-skills by Vercel: github.com/vercel-labs/agent… -> audits your UI against 100+ rules for accessibility, performance and UX SKETCH THE IDEA 06. excalidraw-mcp by Excalidraw: github.com/excalidraw/excali… -> Claude streams hand-drawn diagrams you can open and edit full screen 07. tldraw: github.com/tldraw/tldraw -> an agent starter kit that reads and edits the canvas with you DESIGN ON THE CANVAS 08. mcp-server-guide by Figma: github.com/figma/mcp-server-… -> Claude builds frames, components and auto layout right on your Figma canvas 09. Figma-Context-MCP: github.com/GLips/Figma-Conte… -> feeds your agent real Figma layout data, way better than pasting screenshots 10. cursor-talk-to-figma-mcp by Grab: github.com/grab/cursor-talk-… -> Claude Code reads and edits your Figma file: frames, text, styles, components 11. penpot: github.com/penpot/penpot -> the open-source design platform, now with its own official MCP built in BUILD THE UI 12. shadcn/ui: github.com/shadcn-ui/ui -> its MCP lets Claude browse, search and install components by name 13. magic-mcp by 21st.dev: github.com/21st-dev/magic-mc… -> v0 inside Claude Code: search 10,000+ components or generate new ones 14. storybook: github.com/storybookjs/story… -> its official MCP hands your agent the components you already have 15. onlook: github.com/onlook-dev/onlook -> edit your Next.js app visually and the code changes with it your next design starts with one of these 15
it's f*cking gold Unity just opened their game engines to Claude Code 15 github repos that turn Opus 5.5 into a full game studio: RUN THE WHOLE STUDIO 01. Claude-Code-Game-Studios: github.com/Donchitos/Claude-… -> 49 agents from creative director to QA tester, all in one Claude Code session BUILD IN UNREAL ENGINE 02. UnrealClaude: github.com/Natfii/UnrealClau… -> Claude Code docked in the Unreal Editor, 20+ tools for actors, Blueprints and levels 03. Unreal_mcp: github.com/ChiR24/Unreal_mcp -> your agent spawns actors, imports assets and launches play sessions in Unreal 04. VibeUE: github.com/kevinpbuckley/Vib… -> rides Unreal's own MCP, fixes frame rate drops and sculpts landscapes 05. unreal-engine-skills: github.com/quodsoler/unreal-… -> 31 skills for Unreal C++, every API checked against the 5.8 source 06. UnrealGenAISupport: github.com/prajwalshettydev/… -> Claude, GPT and Gemini inside your game, even NPCs that run as agents 07. Audio2Face-3D by NVIDIA: github.com/NVIDIA/Audio2Face… -> a voice line in, lip-sync and facial animation out, with a UE5 plugin UNITY AND GODOT 08. Unity-MCP: github.com/IvanMurzak/Unity-… -> debugs your project, builds mechanics, and keeps working inside your compiled game 09. ml-agents by Unity: github.com/Unity-Technologie… -> train NPCs that learn by playing, with reinforcement learning and self-play 10. godot-mcp: github.com/Coding-Solo/godot… -> builds scenes, runs your game in debug mode and reads the errors back ART AND SOUND 11. CHORD by Ubisoft: github.com/ubisoft/ubisoft-l… -> one texture image in, base color, normal, height, roughness and metalness maps out 12. ACE-Step 1.5: github.com/ace-step/ACE-Step… -> a soundtrack for your game, under 10 seconds a song on an RTX 3090 13. HunyuanVideo-Foley by Tencent: github.com/Tencent-Hunyuan/H… -> drop in a gameplay clip, get sound effects synced to what's on screen WORLDS THAT GENERATE AS YOU PLAY 14. Hunyuan-GameCraft by Tencent: github.com/Tencent-Hunyuan/H… -> one image plus WASD in, playable game video out, trained on 100+ AAA games 15. Matrix-Game by Skywork: github.com/SkyworkAI/Matrix-… -> a real-time world you can walk through, and it remembers where you've been every department of a game studio, one link each. should i write the full guide to building an Unreal game with Claude?
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