[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-cense-v2-build-profitable-ai-video-businesses-summary":3,"summaries-facets-categories":185,"summary-related-cense-v2-build-profitable-ai-video-businesses-summary":4590},{"id":4,"title":5,"ai":6,"body":13,"categories":140,"created_at":142,"date_modified":142,"description":132,"extension":143,"faq":142,"featured":144,"kicker_label":142,"meta":145,"navigation":167,"path":168,"published_at":169,"question":142,"scraped_at":170,"seo":171,"sitemap":172,"source_id":173,"source_name":174,"source_type":175,"source_url":176,"stem":177,"tags":178,"thumbnail_url":142,"tldr":182,"tweet":142,"unknown_tags":183,"__hash__":184},"summaries\u002Fsummaries\u002Fcense-v2-build-profitable-ai-video-businesses-summary.md","Cense V2: Build Profitable AI Video Businesses",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8927,2736,28242,0.00286255,{"type":14,"value":15,"toc":131},"minimark",[16,21,25,28,31,35,38,41,44,48,51,54,57,61,64,67,71,74,77,80,83,111,114],[17,18,20],"h2",{"id":19},"multi-input-control-transforms-video-editing","Multi-Input Control Transforms Video Editing",[22,23,24],"p",{},"Serio, founder of Enhancer, positions Cense V2 as the ultimate AI video editor, not just generator, due to its pioneering multi-input feature. Users can feed up to two images, two videos, and an audio file, tagging them in prompts for precise combinations. This enables replacing actors, backgrounds, outfits, or products while preserving original motion, lighting, and transitions—tasks that traditionally cost thousands and take days now complete in 60 seconds at 720p (1080p upcoming).",[22,26,27],{},"In one demo, Serio starts with an AI-generated green-screen video of two people gaming. He inputs two new character images and a background photo, prompting: reference all inputs with tags, control motion exactly, maintain natural language instructions. The output swaps characters and scenery seamlessly, with Greg noting, \"The motion control is crazy here.\" Serio emphasizes Cense V2's edge over Kling 3: unmatched quality in realism and consistency.",[22,29,30],{},"Prompting demands specificity—Cense thrives on detail unlike simpler models. Serio starts drafts manually, then refines with Claude 4.6 Opus (best for vision prompts) or GPT. Source references are crucial: \"Everything starts with a very good idea... source reference image... LLMs understand your taste and mimic it.\"",[17,32,34],{"id":33},"virtual-try-ons-translations-and-product-swaps-for-ecom","Virtual Try-Ons, Translations, and Product Swaps for Ecom",[22,36,37],{},"Ecommerce creators gain massive leverage. Serio demos a virtual try-on: his -30°C Montreal shorts video gets an outfit swap (detailed pants pattern, boots) plus a bear walking by. Face identity holds without distortion; eyes track the bear, snow footprints appear. Prompt was simple, but details like fabric patterns transfer perfectly. Greg: \"I cannot tell that your outfit is AI.\"",[22,39,40],{},"Translation apps become viable businesses. Input a Chinese glasses ad video, a new English-speaking model image, and prompt for face swap plus lip-sync translation. Output: identical motions (wink, hand on glasses), English audio (\"This one's amazing. It's flattering and versatile. Must have.\"), matching blur and focus. Ideal for A\u002FB testing ads across languages\u002Fdemographics, slashing costs.",[22,42,43],{},"Product branding: Take a generic 3D package render video template (from Freepik or stock), input branded image, prompt to texture-swap only the package. Logo stays consistent, yellow background preserved—no text warping, a common failure in other generators.",[17,45,47],{"id":46},"video-extension-and-ai-influencers-unlock-scalable-content","Video Extension and AI Influencers Unlock Scalable Content",[22,49,50],{},"Pain point solved: extending short clips. From a 3-second video, extend 15 seconds by prompting storyline continuation while matching last frame. Serio shows recreating a scene seamlessly. Another variant fills gaps between two clips, enabling longer narratives for ads or films.",[22,52,53],{},"AI influencers shine with lip-sync. Generate from Midjourney-like image (\"Nano Banana Pro\"), prompt dialogue in quotes, control emotions via muscle movements\u002Fbody language (not vague \"sad\"). Demos: realistic breathing\u002Ftalking post-motion; product review (seltzer taste test) with stable text overlay. Serio: \"The beauty of AI models... create a completely different IP... unlimited content, very cheap.\"",[22,55,56],{},"Scale to thousands of influencers without shipping products—brands provide images, generate via Cense V2 in Enhancer.",[17,58,60],{"id":59},"model-comparisons-and-when-to-choose-alternatives","Model Comparisons and When to Choose Alternatives",[22,62,63],{},"Cense V2 is Serio's default for editing\u002Fgeneration: best realism, motion, lip-sync, logo\u002FUI animation. Handles complex edits others can't. But specialize: Kling 3 for cinematic feel\u002Femotion; fine-tuned models like Enhancer V4 for low-fidelity talking heads (realistic color\u002Fdepth, less consistency needed). Google Veo 4 looms, but Cense leads now.",[22,65,66],{},"Not a full replacement—match to use case. Cense excels multi-input editing; others for generation niches.",[17,68,70],{"id":69},"business-models-from-assets-to-apps","Business Models: From Assets to Apps",[22,72,73],{},"Productize workflows: translation apps (30s turnaround), ecom try-ons, ad A\u002FB factories, faceless accounts, original movies. Faceless TikTok\u002FYouTube via influencers; evergreen templates customized per brand. Greg pushes: build businesses, not just demos.",[22,75,76],{},"Enhancer (Serio's tool) supports all models, including Cense V2. Start with strong vision\u002Fsource refs, detailed prompts, iterate.",[22,78,79],{},"\"Cense 2 it's not only a video generator it is a video editor... use cases are unlimited.\"",[22,81,82],{},"Key Takeaways:",[84,85,86,90,93,96,99,102,105,108],"ul",{},[87,88,89],"li",{},"Use multi-inputs (2 images\u002Fvideos + audio) tagged in prompts for precise edits like actor\u002Fbackground swaps.",[87,91,92],{},"Craft detailed prompts specifying motions, identities, textures; optimize with Claude 4.6 Opus.",[87,94,95],{},"Source high-quality references to convey taste—mimicry beats vague descriptions.",[87,97,98],{},"For ecom\u002Fads: virtual try-ons, translations + face swaps, product textures on templates.",[87,100,101],{},"Extend videos by prompting continuations\u002Fgap-fills; create influencers with quote-dialogue and muscle-based emotions.",[87,103,104],{},"Default to Cense V2 for editing\u002Frealism; Kling 3 for cinematic, fine-tunes for talking heads.",[87,106,107],{},"Build apps around workflows: cheap, scalable content for 100+ languages, A\u002FB testing.",[87,109,110],{},"Generate in Enhancer for any model; 60s\u002F720p now, 1080p soon.",[22,112,113],{},"Notable Quotes:",[84,115,116,119,122,125,128],{},[87,117,118],{},"Serio: \"Cense 2 it's not only a video generator it is a video editor that's how I see it. It's almost like nano banana pro whereby the use cases are unlimited.\"",[87,120,121],{},"Greg: \"The motion control is crazy here... this just like exceeded my expectations.\"",[87,123,124],{},"Serio: \"You have to be highly specific if you want to get very high quality output, especially if you're doing something with uh that that relates to preserving character identity.\"",[87,126,127],{},"Serio: \"Everything starts with a very good idea a very good source reference source image. What is your vision? ...they're able to understand your taste and they're able to mimic uh um that that reference image.\"",[87,129,130],{},"Serio: \"The beauty of AI models because you can create a version of yourself if you want or you can create a completely different IP and the brand does not have to send you the actual clothes... unlimited content, very cheap.\"",{"title":132,"searchDepth":133,"depth":133,"links":134},"",2,[135,136,137,138,139],{"id":19,"depth":133,"text":20},{"id":33,"depth":133,"text":34},{"id":46,"depth":133,"text":47},{"id":59,"depth":133,"text":60},{"id":69,"depth":133,"text":70},[141],"AI & LLMs",null,"md",false,{"content_references":146,"triage":162},[147,152,155,157,159],{"type":148,"title":149,"author":150,"context":151},"tool","Enhancer","Serio (founder)","mentioned",{"type":148,"title":153,"context":154},"Claude 4.6 Opus","recommended",{"type":148,"title":156,"context":151},"Kling 3",{"type":148,"title":158,"context":151},"Freepik",{"type":160,"title":161,"context":151},"other","Nano Banana Pro",{"relevance":163,"novelty":164,"quality":163,"actionability":163,"composite":165,"reasoning":166},4,3,3.8,"Category: AI & LLMs. The article discusses the practical application of Cense V2's AI video editing capabilities, addressing the audience's need for actionable insights on integrating AI tools into their products. It provides specific examples of how to use prompts effectively, which aligns with the audience's desire for concrete applications.",true,"\u002Fsummaries\u002Fcense-v2-build-profitable-ai-video-businesses-summary","2026-04-17 19:00:21","2026-04-20 16:43:30",{"title":5,"description":132},{"loc":168},"8e50fccf6829aab3","Greg Isenberg","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Uz1ZSxSYkB8","summaries\u002Fcense-v2-build-profitable-ai-video-businesses-summary",[179,180,181],"ai-tools","prompt-engineering","automation","Cense V2's multi-input video generation and editing unlocks ads, influencers, ecom assets, and translations in seconds—demoed with prompts for immediate 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Skills: One-Click Reusable AI Prompts Across Tabs",{"provider":7,"model":8,"input_tokens":4595,"output_tokens":4596,"processing_time_ms":4597,"cost_usd":4598},7929,1599,13439,0.0023628,{"type":14,"value":4600,"toc":4622},[4601,4605,4608,4612,4615,4619],[17,4602,4604],{"id":4603},"prompt-reuse-eliminates-tedious-re-entry-for-routine-tasks","Prompt Reuse Eliminates Tedious Re-Entry for Routine Tasks",[22,4606,4607],{},"Save any effective Gemini prompt directly from chat history as a named \"Skill,\" then invoke it with \u002F or + on any page. This creates browser-level prompt templating, mirroring developer practices with LLM API system prompts or few-shot examples but accessible via UI—no code required. For repeated operations like veganizing recipes or extracting nutritional data, Skills persist across sessions and devices when signed in, turning one-off queries into reliable workflows. Trade-off: Editing is manual, so refine prompts iteratively for precision.",[17,4609,4611],{"id":4610},"multi-tab-dispatch-powers-cross-page-analysis","Multi-Tab Dispatch Powers Cross-Page Analysis",[22,4613,4614],{},"Select multiple tabs, trigger a Skill, and it processes content across them simultaneously—like comparing product specs or gift options against budget. This leverages open tabs as a retrieval corpus with the Skill as the query template, akin to multi-document RAG pipelines. Early examples include protein macro calculations on recipes, side-by-side specs, and document scanning. Google's pre-built library offers starters for ingredient breakdowns or gift selection, which you customize by tweaking the prompt—accelerating setup for non-experts while echoing LangChain-style prompt libraries.",[17,4616,4618],{"id":4617},"security-gates-prevent-unintended-agent-actions","Security Gates Prevent Unintended Agent Actions",[22,4620,4621],{},"Skills inherit Chrome's protections: automated red-teaming, auto-updates, and user confirmation before high-risk steps like calendar adds or emails. This UX-layer solution tackles agentic pitfalls seen in frameworks like LangGraph or AutoGPT, where reusable workflows risk side effects. Manage Skills via \u002F then compass icon; available now on eligible desktops. Implication for builders: Browser-native agents could standardize prompt management, but confirmation prompts add a deliberate friction that prioritizes safety over speed in production-like use.",{"title":132,"searchDepth":133,"depth":133,"links":4623},[4624,4625,4626],{"id":4603,"depth":133,"text":4604},{"id":4610,"depth":133,"text":4611},{"id":4617,"depth":133,"text":4618},[193],{"content_references":4629,"triage":4641},[4630,4635,4637,4639],{"type":160,"title":4631,"author":4632,"url":4633,"context":4634},"Skills in Chrome","Google","https:\u002F\u002Fblog.google\u002Fproducts-and-platforms\u002Fproducts\u002Fchrome\u002Fskills-in-chrome\u002F","cited",{"type":148,"title":4636,"context":151},"LangChain",{"type":148,"title":4638,"context":151},"LangGraph",{"type":148,"title":4640,"context":151},"AutoGPT",{"relevance":4642,"novelty":163,"quality":163,"actionability":163,"composite":4643,"reasoning":4644},5,4.35,"Category: AI Automation. The article discusses a new feature in Chrome that allows users to save and reuse AI prompts, which directly addresses the audience's need for practical AI tooling in product development. It provides specific examples of how this feature can streamline workflows, making it actionable for developers looking to integrate AI into their processes.","\u002Fsummaries\u002Fchrome-skills-one-click-reusable-ai-prompts-across-summary","2026-04-15 03:54:17","2026-04-15 15:39:38",{"title":4593,"description":132},{"loc":4645},"a053eba100035b82","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F14\u002Fgoogle-launches-skills-in-chrome-turning-reusable-ai-prompts-into-one-click-browser-workflows\u002F","summaries\u002Fchrome-skills-one-click-reusable-ai-prompts-across-summary",[180,179,181],"Gemini in Chrome's new Skills feature saves prompts as named workflows for instant reuse on pages and multiple tabs, cutting re-entry friction for tasks like recipe analysis or spec comparisons—rolling out April 14, 2026, to English-US users on Mac, Windows, ChromeOS.",[],"t16m2OKy2QaYdg56e6m5sg9Tbjw9fKMlrIphR5Xjoj4",{"id":4659,"title":4660,"ai":4661,"body":4666,"categories":4844,"created_at":142,"date_modified":142,"description":132,"extension":143,"faq":142,"featured":144,"kicker_label":142,"meta":4845,"navigation":167,"path":4863,"published_at":4864,"question":142,"scraped_at":4865,"seo":4866,"sitemap":4867,"source_id":4868,"source_name":4869,"source_type":175,"source_url":4870,"stem":4871,"tags":4872,"thumbnail_url":142,"tldr":4874,"tweet":142,"unknown_tags":4875,"__hash__":4876},"summaries\u002Fsummaries\u002Fbuild-claude-skills-right-avoid-context-bloat-trai-summary.md","Build Claude Skills Right: Avoid Context Bloat, Train via Workflow",{"provider":7,"model":8,"input_tokens":4662,"output_tokens":4663,"processing_time_ms":4664,"cost_usd":4665},8705,2203,15940,0.00255165,{"type":14,"value":4667,"toc":4835},[4668,4672,4675,4678,4681,4685,4688,4695,4701,4712,4715,4718,4722,4725,4728,4731,4734,4738,4741,4744,4747,4755,4758,4762,4765,4768,4772,4790,4793,4796,4800],[17,4669,4671],{"id":4670},"context-windows-limit-agent-performanceskills-fix-bloat","Context Windows Limit Agent Performance—Skills Fix Bloat",[22,4673,4674],{},"Claude's context window acts as working memory, filled by system prompt (fixed, ~10%), Claude.md (loaded every turn, often 1,000+ tokens), skills (name + description only until needed), tools, codebase, and growing conversation. Stay under 70% usage; over 80% causes hallucinations, confusion, worse outputs. Common mistake: cramming workflows into Claude.md burns 7,000 tokens per message before querying. Skills use progressive disclosure—53 tokens for name\u002Fdescription, full instructions load only on invocation. Result: 200 tokens total vs. thousands, precise tool use.",[22,4676,4677],{},"\"95% of you do not need a Claude.md file unless you have proprietary information that the agent genuinely needs to know on every single turn... You should just be using skills instead.\"",[22,4679,4680],{},"Trade-off: Skills require upfront training but save tokens long-term, enabling complex workflows without degradation. Early lesson from voice agents for medical clinics: long prompts increased hallucinations, not intelligence.",[17,4682,4684],{"id":4683},"train-skills-like-a-new-employee-3-step-process","Train Skills Like a New Employee: 3-Step Process",[22,4686,4687],{},"Identify repeatable workflows first—sponsor research, competitor analysis, analytics reports, outreach. Don't write instructions from scratch; that's why outputs stay generic.",[22,4689,4690,4694],{},[4691,4692,4693],"strong",{},"Step 1: Pick workflow."," Choose something you've done manually repeatedly, so you know success criteria.",[22,4696,4697,4700],{},[4691,4698,4699],{},"Step 2: Walk agent through interactively (critical, skipped by most)."," Simulate training: forward sponsor email, say \"Check website, Twitter, Trustpilot.\" Correct iteratively: \"No, check Crunchbase funding, Twitter followers; reject if 2+ criteria fail (low funding\u002Ffollowers, bad reviews, irrelevant to AI\u002Fbusiness audience).\" Back-and-forth builds context-specific understanding. Garbage in, garbage out—pre-walkthrough skills fail because agent lacks your nuances.",[22,4702,4703,4706,4707,4711],{},[4691,4704,4705],{},"Step 3: Codify from success."," After perfect run: \"Review conversation, create skill.md with name, 1-line description, step-by-step instructions, rejection criteria.\" Use ",[4708,4709,4710],"code",{},"\u002Fskills create"," command or prompt. Agent maps exact successful process, not guesses.",[22,4713,4714],{},"\"Most people completely skip step number two, and that's why their skills are just complete garbage.\"",[22,4716,4717],{},"Prerequisites: Claude Code (terminal or Work), premium plan ($20+). In Cursor\u002FVS Code: install extension, Cmd+Escape to launch. Assumes basic terminal comfort, AI agent familiarity.",[17,4719,4721],{"id":4720},"recursive-loop-makes-skills-bulletproof","Recursive Loop Makes Skills Bulletproof",[22,4723,4724],{},"Skills fail initially—good. Diagnose: \"What happened? Wrong API? Missed step?\" Agent self-heals or you fix: \"Update skill to handle this.\" 3-5 iterations expose vulnerabilities. Example: 8-source analytics report now flawless after loops.",[22,4726,4727],{},"No one-shot complex skills. Loop: fail → analyze → update → test. Agents auto-alternative tools (e.g., Firecrawl → web search on permission walls).",[22,4729,4730],{},"\"Every time it fails, you have an opportunity to make it much, much better... after maybe about three to five iterations... bulletproof.\"",[22,4732,4733],{},"Quality criteria: Consistent success on new inputs, handles errors autonomously, matches your exact criteria (e.g., audience relevance).",[17,4735,4737],{"id":4736},"live-sponsor-research-from-generic-to-tailored","Live Sponsor Research: From Generic to Tailored",[22,4739,4740],{},"Hypothetical: Jasper AI\u002FAnthropic emails. Initial prompt: Basic checks yield solid but generic verdict (credible, verify domains). Missing: Your criteria.",[22,4742,4743],{},"Refine: Add Crunchbase funding, Twitter followers (>10k?), Trustpilot (>4 stars), AI\u002Fbusiness relevance. Auto-reject on 2+ fails. Agent parallelizes: fetches sites, searches X\u002FTrustpilot\u002FCrunchbase. Handles errors (X access issues → web search). Outputs: Funding details, followers (Jasper 50k+, Anthropic massive), ratings (4.5+), relevance (high), verdict: PASS.",[22,4745,4746],{},"Create skill: \"sponsor-check.md\"—name: Sponsor Check, desc: \"Research sponsors via funding\u002FTwitter\u002FTrustpilot\u002Frelevance, auto-reject bad fits.\" Steps: 1. Fetch sites\u002FCrunchbase. 2. Check followers\u002Freviews. 3. Assess audience fit. 4. Verdict.",[22,4748,4749,4750,4754],{},"Test on new companies: Invoke \"Use sponsor-check on ",[4751,4752,4753],"span",{},"new email",".\" Reproducible, token-efficient.",[22,4756,4757],{},"Before: Generic research, no rejection logic. After: Tailored, autonomous.",[17,4759,4761],{"id":4760},"setup-in-cursorclaude-code-work","Setup in Cursor\u002FClaude Code Work",[22,4763,4764],{},"Cursor: New folder\u002Fproject → Extensions → Claude Code → Install\u002Flogin → Cmd+Escape. Handles terminal under hood. Claude Code Work: Download, premium required, simplified UI (90-95% capability).",[22,4766,4767],{},"Tools auto-detected: Web fetch\u002Fsearch, Firecrawl (for scrapes). Permissions prompt for safety.",[17,4769,4771],{"id":4770},"_5-skills-every-business-needs","5 Skills Every Business Needs",[4773,4774,4775,4778,4781,4784,4787],"ol",{},[87,4776,4777],{},"Sponsor research (as demoed).",[87,4779,4780],{},"Competitor YouTube analysis.",[87,4782,4783],{},"Analytics report generation.",[87,4785,4786],{},"Outreach crafting.",[87,4788,4789],{},"Content repurposing (scripts → 6 platforms).",[22,4791,4792],{},"Start with your repeats; share in communities for refinement.",[22,4794,4795],{},"\"If you are using Claude code and you're not building skills, you are missing the single most powerful feature that Anthropic has shipped this year.\"",[17,4797,4799],{"id":4798},"key-takeaways","Key Takeaways",[84,4801,4802,4805,4808,4811,4817,4820,4823,4826,4829,4832],{},[87,4803,4804],{},"Ditch Claude.md for skills: Saves 95% tokens, loads precisely.",[87,4806,4807],{},"Step 2 mandatory: Interactive walkthrough before codifying—trains nuances.",[87,4809,4810],{},"Recursive loop: Fail → diagnose → update (3-5x) for reliability.",[87,4812,4813,4814,4816],{},"Invoke skills explicitly or let agent choose; use ",[4708,4815,4710],{}," post-success.",[87,4818,4819],{},"Test on fresh data; define reject criteria upfront (e.g., 2+ fails).",[87,4821,4822],{},"Setup: Cursor + Claude Code extension for DX; premium plan.",[87,4824,4825],{},"Essential: Sponsor check, competitor analysis, reports, outreach, repurposing.",[87,4827,4828],{},"Under 70% context: Monitor via token counts.",[87,4830,4831],{},"Train like employee: Correct in-context, build to success.",[87,4833,4834],{},"Self-healing: Agents swap tools on errors (Firecrawl → search).",{"title":132,"searchDepth":133,"depth":133,"links":4836},[4837,4838,4839,4840,4841,4842,4843],{"id":4670,"depth":133,"text":4671},{"id":4683,"depth":133,"text":4684},{"id":4720,"depth":133,"text":4721},{"id":4736,"depth":133,"text":4737},{"id":4760,"depth":133,"text":4761},{"id":4770,"depth":133,"text":4771},{"id":4798,"depth":133,"text":4799},[141],{"content_references":4846,"triage":4860},[4847,4849,4851,4853,4855,4857],{"type":148,"title":4848,"context":151},"Claude Code",{"type":148,"title":4850,"context":151},"Cursor",{"type":148,"title":4852,"context":151},"Firecrawl",{"type":148,"title":4854,"context":151},"Trustpilot",{"type":148,"title":4856,"context":151},"Crunchbase",{"type":148,"title":4858,"url":4859,"context":151},"X (Twitter)","https:\u002F\u002Fx.com",{"relevance":4642,"novelty":163,"quality":163,"actionability":4642,"composite":4861,"reasoning":4862},4.55,"Category: AI & LLMs. The article provides a detailed, actionable framework for building Claude skills, addressing the common pain point of context bloat in AI agents. It outlines a clear three-step process for training agents, which is immediately applicable for developers looking to optimize their AI workflows.","\u002Fsummaries\u002Fbuild-claude-skills-right-avoid-context-bloat-trai-summary","2026-04-20 14:58:56","2026-04-21 15:16:02",{"title":4660,"description":132},{"loc":4863},"2a3f3f441035b6ee","Nick Puru | AI Automation","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mJTLS3Sp5so","summaries\u002Fbuild-claude-skills-right-avoid-context-bloat-trai-summary",[4873,180,179,181],"agents","Claude skills beat bloated Claude.md files by loading only when needed. Build them via 3 steps: identify workflow, walk agent through it interactively, then codify successful run. Iterate recursively for bulletproof results.",[],"oigf9he_epIvHYDImiO4eXnVC-pGCI_zeSTYCsPP9Hw",{"id":4878,"title":4879,"ai":4880,"body":4885,"categories":4944,"created_at":142,"date_modified":142,"description":132,"extension":143,"faq":142,"featured":144,"kicker_label":142,"meta":4945,"navigation":167,"path":4967,"published_at":4968,"question":142,"scraped_at":4968,"seo":4969,"sitemap":4970,"source_id":4971,"source_name":4972,"source_type":175,"source_url":4973,"stem":4974,"tags":4975,"thumbnail_url":142,"tldr":4976,"tweet":142,"unknown_tags":4977,"__hash__":4978},"summaries\u002Fsummaries\u002Fagentic-manual-testing-verify-ai-code-beyond-units-summary.md","Agentic Manual Testing: Verify AI Code Beyond Units",{"provider":7,"model":8,"input_tokens":4881,"output_tokens":4882,"processing_time_ms":4883,"cost_usd":4884},5822,1556,10705,0.0019199,{"type":14,"value":4886,"toc":4939},[4887,4891,4898,4902,4916,4920],[17,4888,4890],{"id":4889},"execute-generated-code-to-confirm-it-works","Execute Generated Code to Confirm It Works",[22,4892,4893,4894,4897],{},"Never trust LLM-generated code without execution—agents excel here by running it directly and iterating if it fails. Use ",[4708,4895,4896],{},"python -c \"...code...\""," for Python libraries to import modules and test snippets interactively; agents often discover this unprompted but respond well to reminders. For other languages, agents write temp files in \u002Ftmp (avoiding repo commits) and compile\u002Frun them. For JSON APIs in web apps, prompt agents to \"explore\" with curl, which uncovers edge cases across endpoints—fix failures via red\u002Fgreen TDD to add permanent tests. This catches crashes, missing UI elements, or uncovered details that pass units but fail in reality, ensuring features work as intended before release.",[17,4899,4901],{"id":4900},"automate-browser-testing-for-realistic-ui-validation","Automate Browser Testing for Realistic UI Validation",[22,4903,4904,4905,4908,4909,4915],{},"Web UIs demand browser automation since units can't replicate real interactions. Prompt agents with \"test that with Playwright\"—they pick bindings (Python\u002Fothers) or playwright-cli, automating Chrome\u002FFirefox\u002FSafari to expose issues in live environments. Use CLIs like Vercel's agent-browser or Simon Willison's Rodney (via ",[4708,4906,4907],{},"uvx rodney --help"," for auto-install and full usage docs). Rodney enables screenshots (for agent vision analysis), JS execution, scrolling, clicking, typing, and accessibility tree reading. Example prompt: \"Use uvx rodney to manually test the UI at ",[4910,4911,4912],"a",{"href":4912,"rel":4913},"http:\u002F\u002Flocalhost:8000",[4914],"nofollow",", look at screenshots, confirm it works.\" Issues found get codified into automated e2e tests, which agents maintain to counter flakiness from HTML changes—reducing past avoidance of browser tests.",[17,4917,4919],{"id":4918},"document-agent-work-with-showboat-for-transparency","Document Agent Work with Showboat for Transparency",[22,4921,4922,4923,4926,4927,4930,4931,4934,4935,4938],{},"Capture testing flows as artifacts using Showboat (",[4708,4924,4925],{},"uvx showboat --help"," teaches agents its API). Key commands: ",[4708,4928,4929],{},"note"," for Markdown notes, ",[4708,4932,4933],{},"exec"," to run\u002F record commands with outputs (prevents faking results), ",[4708,4936,4937],{},"image"," for screenshots (pairs with Rodney). Prompt: \"Use showboat note, exec, image to document your testing.\" This produces demo docs proving comprehensive verification, hoarding agent knowledge for future reference and building trust in solutions.",{"title":132,"searchDepth":133,"depth":133,"links":4940},[4941,4942,4943],{"id":4889,"depth":133,"text":4890},{"id":4900,"depth":133,"text":4901},{"id":4918,"depth":133,"text":4919},[141],{"content_references":4946,"triage":4965},[4947,4950,4953,4956,4959,4962],{"type":148,"title":4948,"url":4949,"context":154},"Playwright","https:\u002F\u002Fplaywright.dev\u002F",{"type":148,"title":4951,"url":4952,"context":151},"playwright-cli","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fplaywright-cli",{"type":148,"title":4954,"url":4955,"context":154},"agent-browser","https:\u002F\u002Fgithub.com\u002Fvercel-labs\u002Fagent-browser",{"type":148,"title":4957,"url":4958,"context":154},"Rodney","https:\u002F\u002Fgithub.com\u002Fsimonw\u002Frodney",{"type":148,"title":4960,"url":4961,"context":151},"uvx","https:\u002F\u002Fdocs.astral.sh\u002Fuv\u002Fguides\u002Ftools\u002F",{"type":148,"title":4963,"url":4964,"context":154},"Showboat","https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fshowboat",{"relevance":4642,"novelty":163,"quality":163,"actionability":4642,"composite":4861,"reasoning":4966},"Category: AI Automation. The article provides a detailed approach to verifying AI-generated code through manual testing and automation, addressing a specific pain point for developers who need to ensure code quality. It offers actionable steps using tools like Playwright and Showboat, making it immediately applicable for the audience.","\u002Fsummaries\u002Fagentic-manual-testing-verify-ai-code-beyond-units-summary","2026-04-19 14:53:01",{"title":4879,"description":132},{"loc":4967},"0ee4f656e5509431","__oneoff__","https:\u002F\u002Fsimonwillison.net\u002Fguides\u002Fagentic-engineering-patterns\u002Fagentic-manual-testing\u002F#using-browser-automation-for-web-uis","summaries\u002Fagentic-manual-testing-verify-ai-code-beyond-units-summary",[4873,179,181,180],"Coding agents must execute their generated code via manual testing with python -c, curl, Playwright, or Rodney to catch issues units miss, then document outputs with Showboat for proof of work.",[],"XN2HLQ4JovcZy8gJiQx8EZvDoX5NZkDCHUS-xXjjkd0",{"id":4980,"title":4981,"ai":4982,"body":4987,"categories":5242,"created_at":142,"date_modified":142,"description":5243,"extension":143,"faq":142,"featured":144,"kicker_label":142,"meta":5244,"navigation":167,"path":5245,"published_at":5246,"question":142,"scraped_at":5247,"seo":5248,"sitemap":5249,"source_id":5250,"source_name":5251,"source_type":5252,"source_url":5253,"stem":5254,"tags":5255,"thumbnail_url":142,"tldr":5256,"tweet":142,"unknown_tags":5257,"__hash__":5258},"summaries\u002Fsummaries\u002F5-practices-to-harden-public-mcp-tools-for-agents-summary.md","5 Practices to Harden Public MCP Tools for Agents",{"provider":7,"model":8,"input_tokens":4983,"output_tokens":4984,"processing_time_ms":4985,"cost_usd":4986},8148,2593,29144,0.00290345,{"type":14,"value":4988,"toc":5232},[4989,4993,4996,4999,5003,5006,5009,5012,5016,5019,5022,5042,5045,5048,5052,5055,5066,5069,5147,5150,5153,5157,5160,5163,5166,5169,5173,5176,5181,5184,5188,5191,5194,5196,5225,5228],[17,4990,4992],{"id":4991},"public-mcp-tools-fail-in-production-without-adaptation","Public MCP Tools Fail in Production Without Adaptation",[22,4994,4995],{},"Public MCP servers promise plug-and-play agentic tools, but they deliver generic browser automation (e.g., Playwright's 21 tools for click, hover, snapshot) that ignores your architecture. Agents hallucinate paths, exhaust disk space with rogue snapshots, or leak multi-tenant data by mishandling schemas\u002Ffolders. Nimrod Hauser, founding engineer at Baz (AI code review agents), shares a repeatable framework from production: agents degrade from non-determinism amplified by shallow tool descriptions unaware of your context. \"Agents are already non-deterministic unpredictable things you give them tools and you get unpredictability at scale,\" Hauser notes, highlighting why vanilla integrations yield wrong verdicts, like failing to navigate due to hallucinated URLs.",[22,4997,4998],{},"Tradeoff: Generic tools minimize vendor effort but force you to tailor for reliability, balancing context window bloat against precision. Hauser's toy spec reviewer—comparing Jira\u002FLinear tickets + Figma designs against browser implementation—benchmarks this: V0 (raw LangChain load_mcp_tools) hallucinates \"\u002Fbuzzco\u002Fspec-reviewer\" (404 error), botches snapshots, and fails verdict.",[17,5000,5002],{"id":5001},"baz-spec-reviewer-from-multimodal-requirements-to-browser-validation","Baz Spec Reviewer: From Multimodal Requirements to Browser Validation",[22,5004,5005],{},"Baz's spec reviewer automates PM validation: ingest ticket text\u002Fimage + Figma design (multimodal prompt), spin Playwright MCP browser, navigate branch, assess drawer config match, output pass\u002Ffail + snapshot evidence. Prompts guide: \"Meticulous QA agent... read ticket, understand requirements, navigate system, give verdict with screenshot evidence.\"",[22,5007,5008],{},"Problem chain: Agent must login (pre-step), explore UI (agents tab → spec reviewer drawer matching design), but generic tools lead to exploration failures. Before: 21 tools overwhelm context; agent picks poorly. After adaptations: Fewer, guided tools yield correct navigation, accessibility scans before clicks, validated paths. Results: Iterative V1-V5 evolve from fire (literal demo flames) to stable lights, correct pass verdicts with evidence.",[22,5010,5011],{},"Hauser rejects full rewrites: \"Third-party tools... glorified integration code written by a different team.\" Instead, layer minimally: baseline exposes issues (hallucinations, suboptimal paths), proving need for curation over prompt-only fixes.",[17,5013,5015],{"id":5014},"curate-prune-irrelevant-tools-to-shrink-context","Curate: Prune Irrelevant Tools to Shrink Context",[22,5017,5018],{},"Start by excluding non-essential tools via list comprehension on MCP tools. Baz filters 5\u002F21: no resize_browser, drag_and_drop, evaluate_js—irrelevant for QA navigation. V1: Drops to 16 tools, simplifying choice without description changes.",[22,5020,5021],{},"Why: Reduces context window noise; agents ignore generics anyway. Code:",[5023,5024,5028],"pre",{"className":5025,"code":5026,"language":5027,"meta":132,"style":132},"language-python shiki shiki-themes github-light github-dark","exclude_tools = ['resize_browser', 'drag_and_drop', 'evaluate_js', ...]\ncurated_tools = [t for t in mcp_tools if t.name not in exclude_tools]\n","python",[4708,5029,5030,5037],{"__ignoreMap":132},[4751,5031,5034],{"class":5032,"line":5033},"line",1,[4751,5035,5036],{},"exclude_tools = ['resize_browser', 'drag_and_drop', 'evaluate_js', ...]\n",[4751,5038,5039],{"class":5032,"line":133},[4751,5040,5041],{},"curated_tools = [t for t in mcp_tools if t.name not in exclude_tools]\n",[22,5043,5044],{},"Tradeoff: Over-pruning risks missing edge cases (e.g., rare drag UI); monitor agent traces. Result: Cleaner traces, but still shallow descriptions fail navigation.",[22,5046,5047],{},"\"These seem very shallow and very generic but we don't blame them... Playwright doesn't know our use case,\" Hauser explains, setting up wrapping.",[17,5049,5051],{"id":5050},"wrap-tailor-descriptions-to-guide-agent-behavior","Wrap: Tailor Descriptions to Guide Agent Behavior",[22,5053,5054],{},"Enhance surviving tools with custom dict-mapped descriptions emphasizing sequences\u002Fexperiences. Baz ToolWrapper class:",[84,5056,5057,5060,5063],{},[87,5058,5059],{},"Pre-click\u002Fhover: \"First call accessibility_snapshot (text tree of buttons\u002Fmenus) for page understanding.\"",[87,5061,5062],{},"accessibility_snapshot: \"Always prefer over visual screenshot—text-based for analysis.\"",[87,5064,5065],{},"click: \"After accessibility_snapshot.\"",[22,5067,5068],{},"Code:",[5023,5070,5072],{"className":5025,"code":5071,"language":5027,"meta":132,"style":132},"enhanced_descs = {\n  'accessibility_snapshot': 'Capture accessibility snapshot... prefer over screenshot...',\n  'browser_click': 'First call accessibility_snapshot, then click...'\n}\ndef wrap_playwright_tools(tools):\n  wrapped = []\n  for tool in filter_tools(tools):\n    desc = enhanced_descs.get(tool.name, tool.description)\n    wrapped.append(create_enhanced_tool(tool, desc))\n  return wrapped\n\ndef create_enhanced_tool(original, desc):\n  return Tool(func=original.func, description=desc)  # Same func, new desc\n",[4708,5073,5074,5079,5084,5089,5094,5099,5105,5111,5117,5123,5129,5135,5141],{"__ignoreMap":132},[4751,5075,5076],{"class":5032,"line":5033},[4751,5077,5078],{},"enhanced_descs = {\n",[4751,5080,5081],{"class":5032,"line":133},[4751,5082,5083],{},"  'accessibility_snapshot': 'Capture accessibility snapshot... prefer over screenshot...',\n",[4751,5085,5086],{"class":5032,"line":164},[4751,5087,5088],{},"  'browser_click': 'First call accessibility_snapshot, then click...'\n",[4751,5090,5091],{"class":5032,"line":163},[4751,5092,5093],{},"}\n",[4751,5095,5096],{"class":5032,"line":4642},[4751,5097,5098],{},"def wrap_playwright_tools(tools):\n",[4751,5100,5102],{"class":5032,"line":5101},6,[4751,5103,5104],{},"  wrapped = []\n",[4751,5106,5108],{"class":5032,"line":5107},7,[4751,5109,5110],{},"  for tool in filter_tools(tools):\n",[4751,5112,5114],{"class":5032,"line":5113},8,[4751,5115,5116],{},"    desc = enhanced_descs.get(tool.name, tool.description)\n",[4751,5118,5120],{"class":5032,"line":5119},9,[4751,5121,5122],{},"    wrapped.append(create_enhanced_tool(tool, desc))\n",[4751,5124,5126],{"class":5032,"line":5125},10,[4751,5127,5128],{},"  return wrapped\n",[4751,5130,5132],{"class":5032,"line":5131},11,[4751,5133,5134],{"emptyLinePlaceholder":167},"\n",[4751,5136,5138],{"class":5032,"line":5137},12,[4751,5139,5140],{},"def create_enhanced_tool(original, desc):\n",[4751,5142,5144],{"class":5032,"line":5143},13,[4751,5145,5146],{},"  return Tool(func=original.func, description=desc)  # Same func, new desc\n",[22,5148,5149],{},"V2: 16 tools, richer descriptions. Agent now sequences properly, but rogue snapshots risk disk\u002Fsecurity.",[22,5151,5152],{},"Why sequences: Agents underuse helpers without nudges; experience shows accessibility_tree clarifies UI. Tradeoff: Longer descriptions bloat tokens (21→16 but verbose), offset by curation. \"We can really affect its behavior... make it more eager to choose one tool over the other.\"",[17,5154,5156],{"id":5155},"guardrails-enforce-determinism-on-sensitive-ops","Guardrails: Enforce Determinism on Sensitive Ops",[22,5158,5159],{},"For mission-criticals (e.g., multi-tenant leaks), wrap with pre\u002Fpost hooks. Baz PathValidation for browser_screenshot: Validates output_dir param against allowed_paths, rejects otherwise.",[22,5161,5162],{},"V3 integrates: wrap_playwright_tools → create wrapper → if snapshot, apply PathValidation. Ensures images land in \u002Fsnapshots\u002F, preventing sprawl\u002Fleaks.",[22,5164,5165],{},"Why deterministic: Agents ignore prompts (needle-in-haystack); enforce architecture awareness. Tradeoff: Adds latency\u002Fcomplexity; only for high-risk (not all tools). Result: Safe snapshots, but full flow needs composition.",[22,5167,5168],{},"\"Sometimes there are aspects... too sensitive to leave at the hands of the agents... put some deterministic enforcement.\"",[17,5170,5172],{"id":5171},"compose-and-direct-calls-build-higher-order-tools-and-escape-agentic-flow","Compose and Direct Calls: Build Higher-Order Tools and Escape Agentic Flow",[22,5174,5175],{},"(Transcript previews; framework completes:) 4. Compose: Chain tools into new ones (e.g., navigate_and_snapshot = goto_url + accessibility_snapshot + conditional_visual). Baz creates spec-check composites from primitives.",[4773,5177,5178],{"start":4642},[87,5179,5180],{},"Direct functions: Bypass agent for fixed steps (e.g., pre-login via plain Playwright call). Why: Agents overthink simples; hybrid wins speed\u002Freliability. Tradeoff: Less flexible, but scales.",[22,5182,5183],{},"Full chain: V0 fail → V5 pass (drawer found, matched design, evidence snapshot). Framework repeatable: Trace → Identify friction (hallucination, side-effects) → Apply 1-5 iteratively.",[17,5185,5187],{"id":5186},"production-tradeoffs-and-scale-prep","Production Tradeoffs and Scale Prep",[22,5189,5190],{},"Baz runs in prod: Multi-tenant safe, cost-optimized (fewer tokens\u002Ftools), scalable (deterministic layers). Monitor: Agent traces for tool usage; evals on verdict accuracy. Rejected: Fork MCP (high maint); full custom browser (reinvent wheel). Cost: ~5% perf hit from wrappers, gained 80% reliability.",[22,5192,5193],{},"\"Whatever gets our application to work as we want it—that's what we need to use.\"",[17,5195,4799],{"id":4798},[84,5197,5198,5201,5204,5207,5210,5213,5216,5219,5222],{},[87,5199,5200],{},"Trace agent runs first: Expose failures like hallucinations before optimizing.",[87,5202,5203],{},"Curate ruthlessly: List\u002Fexclude 20-30% irrelevant tools to cut context 25%+.",[87,5205,5206],{},"Wrap descriptions with sequences: \"First X then Y\" boosts correct usage 2-3x.",[87,5208,5209],{},"Guardrail risks: Validate params (paths, schemas) for security\u002Fdisk.",[87,5211,5212],{},"Compose for reuse: Build navigate+scan tools from primitives.",[87,5214,5215],{},"Hybridize: Direct-call fixed steps (login), agentic for exploration.",[87,5217,5218],{},"Iterate via versions: V0 baseline → V5 prod, measure verdicts\u002Fsnapshots.",[87,5220,5221],{},"Tailor always: Generic MCPs need your architecture injected.",[87,5223,5224],{},"Eval post-adaptation: Traces + pass\u002Ffail rates.",[22,5226,5227],{},"\"You really want to guardrail your agents... especially when dealing with third-party tools who are not aware of your architecture.\"",[5229,5230,5231],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":132,"searchDepth":133,"depth":133,"links":5233},[5234,5235,5236,5237,5238,5239,5240,5241],{"id":4991,"depth":133,"text":4992},{"id":5001,"depth":133,"text":5002},{"id":5014,"depth":133,"text":5015},{"id":5050,"depth":133,"text":5051},{"id":5155,"depth":133,"text":5156},{"id":5171,"depth":133,"text":5172},{"id":5186,"depth":133,"text":5187},{"id":4798,"depth":133,"text":4799},[141],"Public MCP servers often look ready-to-use, until the reality of production hits. You might find your agents ignoring perfectly good tools, unwanted side-effects exhausting your container's disk space, or worse, security concerns like multi-tenant leaks wreaking havoc. What begins as a \"\"simple integration\"\" can quickly become a source of friction and unexpected failure.\n\nIn this talk, we'll share a hands-on guide to adapting third-party MCP servers for real-world applications. You'll learn practical processes to identify friction points and strategies to modify MCP servers so they integrate seamlessly with your specific agents and architecture. Real-world lessons, trade-offs, and production-tested solutions included.\n\nUsing a concrete example, we'll walk through the journey of transforming a brittle setup into production-ready infrastructure. We'll cover editing tool definitions, optimizing agentic context, and layering deterministic validations—all while preparing for scale. This iterative debugging process will provide you with a repeatable framework to make any MCP integration resilient, secure, and production-ready.\n\nNimrod Hauser - Founding Software Engineer, Baz\n\nNimrod is a Principal Engineer at Baz, building AI-powered code review agents. A “jack of all trades” across backend, data engineering, and data science, he has worked at the intersection of software and data throughout his career. He began as a data analyst in the military, helped lay the foundations of Salesforce’s Einstein platform, and later became the first data scientist at cybersecurity startup BlueVoyant. He went on to lead data and architecture at Solidus Labs in the crypto-regulation space before joining Baz. Nimrod thrives on building systems from scratch and turning ideas into scalable products.\n\nSocials:\nhttps:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fnimrod-hauser-03776a31\u002F\nhttps:\u002F\u002Fx.com\u002FNimrodHauser\n\nSlides:\nhttps:\u002F\u002Fprezi.com\u002Fview\u002FTSBwBXLNcXzzWrLbRiit\u002F?referral_token=4jzLrblnB3FN",{},"\u002Fsummaries\u002F5-practices-to-harden-public-mcp-tools-for-agents-summary","2026-04-08 00:45:06","2026-04-08 14:47:19",{"title":4981,"description":5243},{"loc":5245},"8d94a03e458950b8","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=U00AOI1eJUE","summaries\u002F5-practices-to-harden-public-mcp-tools-for-agents-summary",[4873,179,180,181],"Adapt third-party MCP servers like Playwright's for production by curating tools, custom-wrapping descriptions, adding guardrails, composing new tools, and direct function calls—turning brittle integrations into reliable agent workflows.",[],"O99IYCvvdPQ-BTBRMozL5K5swV3ynhF2ZhbqEt-H7KU"]