[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-189ee40309278936-4-agent-skills-automating-marketing-workflows-summary":3,"summaries-facets-categories":163,"summary-related-189ee40309278936-4-agent-skills-automating-marketing-workflows-summary":3748},{"id":4,"title":5,"ai":6,"body":13,"categories":137,"created_at":139,"date_modified":139,"description":140,"extension":141,"faq":139,"featured":142,"kicker_label":139,"meta":143,"navigation":144,"path":145,"published_at":146,"question":139,"scraped_at":147,"seo":148,"sitemap":149,"source_id":150,"source_name":151,"source_type":152,"source_url":153,"stem":154,"tags":155,"thumbnail_url":139,"tldr":160,"tweet":139,"unknown_tags":161,"__hash__":162},"summaries\u002Fsummaries\u002F189ee40309278936-4-agent-skills-automating-marketing-workflows-summary.md","4 Agent Skills Automating Marketing Workflows",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8620,2721,29646,0.003062,{"type":14,"value":15,"toc":127},"minimark",[16,21,25,28,31,35,38,41,44,47,51,54,57,60,63,67,70,73,76,80,83,86,89,92,96],[17,18,20],"h2",{"id":19},"systems-thinking-turns-marketing-into-delegable-agent-skills","Systems Thinking Turns Marketing into Delegable Agent Skills",[22,23,24],"p",{},"Builders excel at spotting repeatable processes in code but often neglect marketing due to its pull away from core work. Brian Casel reframes marketing as patterned workflows ripe for AI agents, using OpenClaw on a Mac Mini to run four custom skills via Claude. The key decision: identify manual tasks like scanning feeds or drafting emails, then encode them as skills with training data, references, and step-by-step instructions. This avoids becoming a \"world-class marketer\" by delegating 80% of the grunt work.",[22,26,27],{},"Casel rejected generic AI tools for bespoke skills tailored to his Builder Methods brand (newsletters, YouTube, Pro membership). Tradeoffs: Initial setup time (hours per skill) yields daily automation, but requires maintenance like updating RSS feeds. He stores training in markdown (voice\u002Ftone, topics) or custom apps like SparkDrop, pulling interests from years of Claude interactions. Skills reference files for sources, formats, and outputs, ensuring consistency. \"We're builders. We think in systems, processes, repeatable workflows. And now that we can delegate to agents... we can apply that same systems mindset to marketing our business without needing to become worldclass marketers ourselves.\" (Brian Casel, introducing the philosophy—shifts builders from resistance to empowerment.)",[22,29,30],{},"Every role should hunt patterns: Casel urges teams to convert them into skills, scaling beyond solo use. He open-sources tools like Agent-OS and Design-OS at buildermethods.com\u002Fagent-os, providing starter kits for replication.",[17,32,34],{"id":33},"radar-scan-automated-daily-industry-intelligence-without-doomscrolling","Radar Scan: Automated Daily Industry Intelligence Without Doomscrolling",[22,36,37],{},"Problem: Manually tracking AI influencers (Anthropic, OpenAI, Cursor teams) via Twitter\u002FX wastes hours. Casel built \"Content Radar Scan\" to run at 4 AM, delivering a curated Markdown report by wake-up.",[22,39,40],{},"Process: Agent Veil fetches training from SparkDrop\u002Fmarkdown (voice, interests), scans RSS.app XML feeds (free\u002Fpaid tiers) for 24-hour activity. Feeds track team tweets via searches like \"from:anthropic OR from:whatever\" converted to RSS. Agent filters for relevance (e.g., new releases, builder-relevant ideas), discards noise (promos, off-topic), summarizes into briefs with links, and outputs to dated Markdown (e.g., \"2023-10-05-radar-scan.md\") synced via Dropbox. Notifies via Telegram (agent channel separate from humans) with overview\u002Flink to Brainown app for reading\u002Fediting.",[22,42,43],{},"Why RSS.app? Handles Twitter searches scalably; XML is agent-friendly. Rejected full Twitter API for simplicity\u002Fcost. Results: Fuels content ideas for YouTube\u002Fnewsletter without daily scrolling. Tradeoff: Agent judgment imperfect—requires tuning filters, but 90% hit rate per Casel. Evolution: Started with static markdown training; now dynamic via SparkDrop.",[22,45,46],{},"\"My agent kind of collects those and analyzes a lot of like incoming feeds and then decides which ones the agent thinks based on my training are most relevant for me and my business and could potentially become material that I might use or comment on.\" (Brian Casel, explaining filtering—highlights training's role in personalization over raw volume.)",[17,48,50],{"id":49},"brand-illustrator-consistent-visuals-from-concept-to-generation","Brand Illustrator: Consistent Visuals from Concept to Generation",[22,52,53],{},"Opportunity: Public-facing assets (website, workshops, social) need unified style (coral colors, line art, shadows) without design hires. Casel created a skill in a \"BM Brand Illustrations\" project, blending Claude for ideation with Google Gemini API for rendering (Claude lacks native image gen).",[22,55,56],{},"Workflow: Manual trigger in Claude project. User dictates need (e.g., \"illustration for marketing skills article: agents toolbox\"). Agent interviews: context\u002Fscreenshot? Colors (predefined palette)? Size\u002Fscene? Generates 3 detailed concepts (prompt-ready descriptions). User picks (e.g., Option A); agent creates dated subfolder with project.md (logs choices\u002Fprompt), generates V1 image, iterates on feedback.",[22,58,59],{},"References: Colors.md, idea-mapping.md, visual-world.md (object styles). Why iterative concepts? Prevents prompt drift, ensures brand fit. Tradeoff: Not fully automated (human greenlights), but 2-5 iterations yield production-ready assets matching site illustrations. Used for Pro sessions thumbnails—feels \"connected\" across months.",[22,61,62],{},"\"I think that's a really important part of branding and marketing in general.\" (Brian Casel, on style consistency—counters one-off designs that dilute identity.)",[17,64,66],{"id":65},"newsletter-pipeline-from-voice-braindump-to-scheduled-send","Newsletter Pipeline: From Voice Braindump to Scheduled Send",[22,68,69],{},"Pain: Manual ConvertKit (kit.com) drafting\u002Fformatting for Builder Briefing (5-min weekly read at buildermethods.com) was tedious. Now, Claude skills handle 90%: content, subject, sections, export.",[22,71,72],{},"Two skills in \"BM Newsletter\" project: (1) Newsletter Writer—reviews recent examples for style, voice-dicts main message (20-min walk braindump), drafts mini-article, suggests subjects\u002Fpreheaders (pick\u002Frevise), queries sections (YouTube promo? Current build?). 3-5 revisions, final hand-edit. (2) Implied exporter to Kit format (truncated, but skips UI setup).",[22,74,75],{},"Why voice-first? Captures raw ideas; agent polishes. Tradeoff: Still human oversight for tone, but cuts hours to minutes. Results: Consistent issues (numbered, dated, minimal HTML: mini-article, video, builds, signoff).",[17,77,79],{"id":78},"video-repurposing-sponsor-enabled-pipeline-extension","Video Repurposing: Sponsor-Enabled Pipeline Extension",[22,81,82],{},"Bottleneck: Post-recording edits for shorts (YouTube, LinkedIn). Casel spotlights WayinVideo API (wayin.ai): Upload long-form\u002FYouTube link, AI clips engaging moments, reframes vertical, adds captions. Pay-as-you-go REST API; OpenClaw skill on Clawhub automates upload\u002Fjob\u002Fpull\u002Fdeliver.",[22,84,85],{},"Fit: Agent grabs new YouTube video, processes to clips for review\u002Fpost. Why API over UI? Integrates into pipelines like radar\u002Fnewsletter. Tradeoff: Cost per minute, but scales build-in-public.",[22,87,88],{},"\"The bigger idea: every role in your org should be looking for patterns to convert into Skills that agents can own.\" (Brian Casel, core thesis—applies to video, extending beyond his four skills.)",[22,90,91],{},"\"You can literally just describe what you want to do and what your business goals are. Explain those to Claude or Chatubt or Gemini and have it help you build out a custom skill that fits your specific needs for your processes.\" (Brian Casel, on skill-building—democratizes via natural language, no deep coding needed.)",[17,93,95],{"id":94},"key-takeaways","Key Takeaways",[97,98,99,103,106,109,112,115,118,121,124],"ul",{},[100,101,102],"li",{},"Scan for patterns in your marketing: List manual tasks (feeds, visuals, emails), break into steps, encode as OpenClaw\u002FClaude skills with training refs.",[100,104,105],{},"Use RSS.app for social feeds: Convert Twitter searches to XML; filter via agent instructions tuned to your niche.",[100,107,108],{},"Build brand refs once: Colors\u002Fobjects in markdown; Claude ideates, external API (Gemini) generates—iterate 3 concepts first.",[100,110,111],{},"Voice-dictate for newsletters: Braindump → agent draft → revise; reference past issues for evolving style.",[100,113,114],{},"Integrate APIs like WayinVideo: Plug into agents for post-production; start with Clawhub skills.",[100,116,117],{},"Separate comms: Telegram channels for agents; Dropbox\u002FBrainown for file handoff.",[100,119,120],{},"Train incrementally: Start with markdown voice\u002Ftopics; evolve to apps like SparkDrop.",[100,122,123],{},"Review daily: Agents handle 80-90%, but human loop for judgment\u002Fcontent quality.",[100,125,126],{},"Open-source starters: Fork Casel's Agent-OS\u002FDesign-OS; join Builder Methods Pro for dashboards\u002Fcourses.",{"title":128,"searchDepth":129,"depth":129,"links":130},"",2,[131,132,133,134,135,136],{"id":19,"depth":129,"text":20},{"id":33,"depth":129,"text":34},{"id":49,"depth":129,"text":50},{"id":65,"depth":129,"text":66},{"id":78,"depth":129,"text":79},{"id":94,"depth":129,"text":95},[138],"AI Automation",null,"I walk through four custom agent Skills I've built to handle the marketing side of my business. Each one started as a repeatable process I was doing by hand — now they're delegated to my agents. The bigger idea: every role in your org should be looking for patterns to convert into Skills that agents can own.\n\nTry WayinVideo for AI video editing & API to power your agent’s video skills!  https:\u002F\u002Fwayin.ai\n\n👇 **Your Builder Briefing (free)**\nhttps:\u002F\u002Fbuildermethods.com - Your free, 5-minute read to keep up with the latest tools & workflows for building with AI.\n\n👇 **Join Builder Methods Pro**\nhttps:\u002F\u002Fbuildermethods.com\u002Fpro - The membership for pros building with AI.  Courses.  Workshops.  Private community.  Video training library.\n\n👇 **Try my tools** (free open source):\nhttps:\u002F\u002Fbuildermethods.com\u002Fagent-os\nhttps:\u002F\u002Fbuildermethods.com\u002Fdesign-os\n\n▶️ Related videos:\nMy Multi-Agent Team with OpenClaw: https:\u002F\u002Fyoutu.be\u002FbzWI3Dil9Ig\nCreate JOBS for OpenClaw Agents: https:\u002F\u002Fyoutu.be\u002FuUN1oy2PRHo\n\n💬 Drop a comment with your questions and requests for upcoming videos!\n\nChapters:\n\n00:00 Marketing Skills for Agents\n01:29 Skill 1\n08:53 Skill 2\n14:25 Skill 3\n16:22 Skill 4\n23:04 Bonus skill","md",false,{},true,"\u002Fsummaries\u002F189ee40309278936-4-agent-skills-automating-marketing-workflows-summary","2026-04-06 12:00:44","2026-04-06 16:43:28",{"title":5,"description":140},{"loc":145},"189ee40309278936","Brian 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This mirrors information theory's entropy: systems feeding on themselves degrade signal into noise, as seen in AI models trained on AI text collapsing quality (Nature study). Psychologically, creators avoid experimentation, sticking to safe, generic frameworks.",[22,3767,3768],{},"Breakthroughs require Edward de Bono's lateral thinking: introduce random constraints from outside the niche, like solving marketing via 19th-century naval tactics or Pixar feedback sessions. Sherlock Holmes catalogs data flawlessly but can't storytell; John Watson adds human context, emotional resonance, and cultural weight. Build agents that emulate Watson: reject keyword-matching, demand structural similarities (not surface resemblances), and filter through your brand positioning (voice DNA, audience profiles, content pillars from Notion\u002FGoogle Drive).",[22,3770,3771],{},"Discard shallow connections per rules like \"No stretching logic\" or \"If ChatGPT would suggest it, kill it.\" Target idea types: explainer-with-depth (trend + psych framework + brand tie), contrarian (mainstream + opposition + reframe), unexpected analogy (unrelated domain mapped to topic). Score on timeliness, originality, brand fit, combo strength, engagement (High\u002FMedium\u002FLow).",[17,3773,3775],{"id":3774},"modular-claude-code-architecture-separates-identity-rules-and-skills-for-scalable-agents","Modular Claude Code Architecture Separates Identity, Rules, and Skills for Scalable Agents",[22,3777,3778],{},"Ditch single 300-line Markdown files; use a directory structure for any Claude Code agent:",[3780,3781,3786],"pre",{"className":3782,"code":3784,"language":3785},[3783],"language-text","agent-name\u002F\n├── CLAUDE.md              # Identity, mission, capabilities\n├── claude\u002F\n│   ├── rules\u002F             # Always-on constraints (fire first)\n│   └── skills\u002F            # On-demand workflows\n├── inbox\u002F, outputs\u002F, archive\u002F\n","text",[3787,3788,3784],"code",{"__ignoreMap":128},[22,3790,3791,3795],{},[3792,3793,3794],"strong",{},"Identity (CLAUDE.md):"," Define as \"senior content strategist specializing in cross-domain ideation.\" Mission: non-obvious world-to-brand connections. Core principle: despise generic takes.",[22,3797,3798],{},[3792,3799,3800],{},"Rules (always-on, 5 files):",[97,3802,3803,3806,3809,3812,3815],{},[100,3804,3805],{},"00-onboarding.md: Locate brand docs or halt.",[100,3807,3808],{},"01-scope-assessment.md: Searchable topic? Research. Personal? Ask once: research themes or riff on brand?",[100,3810,3811],{},"02-execution-rules.md: Enforce \"diverse ideas only,\" \"sacred connection paragraph\" proving structural similarity, no shallow combos.",[100,3813,3814],{},"03-data-source-config.md: Read all brand files every run.",[100,3816,3817],{},"04-reddit-crawling.md: Bypass Reddit blocks via Markdown converter for unfiltered opinions.",[22,3819,3820],{},[3792,3821,3822],{},"Skills (on-demand):",[97,3824,3825,3828,3831],{},[100,3826,3827],{},"00-setup-datasource.md: One-time brand doc validation.",[100,3829,3830],{},"01-idea-generation-pipeline.md (6 steps): 1) Sweep 20+ sources (broad queries + targeted: Reddit, HN, papers, blogs). 2) Categorize into 5 always-on lenses (news, opinions, contrarians, psych\u002Fbehavior, analogies) + 8 conditional (business, tech, culture, history, data, regs, creators, failures). 3) Build 15-30 row table (tagged findings, no early filter). 4) Load brand docs. 5) Cross-pollinate for surprise\u002Fnovelty. 6) Score ideas.",[100,3832,3833],{},"02-output-format.md: Per idea—type, angle, connection para, title\u002Fsubtitle\u002Fhook, controversy, scores, sources, adjacents.",[22,3835,3836,3837,3840],{},"Build an \"Agent Optimizer\" skill first: input bulky file, outputs modular structure. YAML header auto-loads: ",[3787,3838,3839],{},"name: watson-editorial-researcher",", model: opus, memory: project. Quick test prompt: Find 3 connections (psych, unrelated domain, Reddit) with structural similarity explanations.",[17,3842,3844],{"id":3843},"watson-generates-high-impact-ideas-nano-banana-2-examples","WATSON Generates High-Impact Ideas: Nano Banana 2 Examples",[22,3846,3847],{},"Input: \"Nano Banana 2\" (Google's fast AI image model). Output: 25+ sources, 11 categories, 25-row table, 5 ideas.",[22,3849,3850,3853],{},[3792,3851,3852],{},"Idea 1: Visual Elevator Music Problem"," (High scores)—Links ScienceDirect paper (700 trajectories converging to identical outputs over 100 iterations, termed \"visual elevator music\") to NB2's 4-8s speed and brand fear of dilution. Connection: \"Faster tools accelerate uninterrupted iteration toward homogenized slop, surrendering taste quicker.\"",[22,3855,3856,3859],{},[3792,3857,3858],{},"Idea 2: Google Solved Face Consistency; Fix Your Voice Drift","—NB2 maintains 5 characters' appearances across workflows via stable reference architecture. Analogy: Text AI forgets your voice unless you build identity-holding systems (e.g., brand docs). No summarizer links image tech to text voice stability.",[22,3861,3862],{},"Pre-WATSON: Rush to generic features\u002Fuse-cases. Post: ScienceDirect loops, de Bono lateral thinking, Reddit friction, voice consistency analogy—unique angles preserving creator voice while covering news.",{"title":128,"searchDepth":129,"depth":129,"links":3864},[3865,3866,3867],{"id":3761,"depth":129,"text":3762},{"id":3774,"depth":129,"text":3775},{"id":3843,"depth":129,"text":3844},[138],{},"\u002Fsummaries\u002Fbuild-watson-lateral-ai-agent-for-original-content-summary","2026-04-08 21:21:17",{"title":3751,"description":128},{"loc":3870},"f606ae8856f59bca","Robots Ate My Homework","article","https:\u002F\u002Funknown","summaries\u002Fbuild-watson-lateral-ai-agent-for-original-content-summary",[156,157,3880,159],"prompt-engineering","Replace boring AI summaries with WATSON, a Claude Code agent that cross-pollinates 20+ broad sources against your brand docs to generate novel, non-obvious content angles via lateral thinking.",[159],"FLoz30GbpvDd9tLZt9yZcAvFbK4Vo-SbXbKNzWnebes",{"id":3885,"title":3886,"ai":3887,"body":3892,"categories":4058,"created_at":139,"date_modified":139,"description":4059,"extension":141,"faq":139,"featured":142,"kicker_label":139,"meta":4060,"navigation":144,"path":4061,"published_at":4062,"question":139,"scraped_at":4063,"seo":4064,"sitemap":4065,"source_id":4066,"source_name":4067,"source_type":152,"source_url":4068,"stem":4069,"tags":4070,"thumbnail_url":139,"tldr":4072,"tweet":139,"unknown_tags":4073,"__hash__":4074},"summaries\u002Fsummaries\u002F7ad97a9dc97bdeb8-self-improving-linkedin-pipeline-with-claude-code--summary.md","Self-Improving LinkedIn Pipeline with Claude Code & Autoresearch",{"provider":7,"model":8,"input_tokens":3888,"output_tokens":3889,"processing_time_ms":3890,"cost_usd":3891},8693,2494,16458,0.0029631,{"type":14,"value":3893,"toc":4051},[3894,3898,3901,3904,3907,3911,3914,3935,3938,3964,3967,3970,3973,3977,3984,3987,3990,4001,4004,4007,4011,4014,4017,4020,4022],[17,3895,3897],{"id":3896},"from-manual-posts-to-autonomous-content-flywheel","From Manual Posts to Autonomous Content Flywheel",[22,3899,3900],{},"Duncan Rogoff, a former art director for Apple, PlayStation, and Nissan now running a six-figure AI agency, faced the challenge of scaling LinkedIn content manually. His posts garnered 4,500 to 90,000 impressions, driving social proof, leads, and community signups to Buildroom (his Skool community for AI-powered personal brands). But consistency required hands-on effort: crafting lead magnets in Notion, writing posts, recording 6-7 second scroll videos with Gotham font overlays (ultra\u002Fmedium weights, neon green box with black stroke), and overlaying captions. The opportunity? Automate end-to-end while making it self-improving via Karpathy's autoresearch concept—a feedback loop where engagement metrics (hooks, formats, lengths, angles, topics) refine future outputs.",[22,3902,3903],{},"He rejected pure manual scaling or basic automation without learning. Instead, he chose Claude Code in Antigravity IDE for its plan mode, agent teams, browser capabilities, and parallel execution. Tradeoffs: Higher token costs for agent teams (multiple sub-agents collaborating) vs. single-agent speed, but faster cohesive builds (10 minutes total). GitHub Actions for scheduling beat local runs for reliability, though requiring secure secrets storage (API keys). Apify scraper ($5\u002F1,000 results, $5 free monthly credit) over direct LinkedIn API or Claude's browser scraping for simplicity and proven JSON outputs (post text, URL, reactions, likes, comments).",[22,3905,3906],{},"\"Claude Code is changing the way I do everything and the way I run my business.\" – Duncan introduces the build, highlighting its business leverage for a technical founder juggling agency work.",[17,3908,3910],{"id":3909},"architecture-daily-generation-weekly-optimization-loop","Architecture: Daily Generation + Weekly Optimization Loop",[22,3912,3913],{},"The system runs three GitHub Actions:",[3915,3916,3917,3923,3929],"ol",{},[100,3918,3919,3922],{},[3792,3920,3921],{},"Daily Pipeline (9 AM)",": Scrapes Reddit for trending topics (audience-aligned: experts with low online visibility seeking AI content strategies). Claude Code's lead magnet skill generates Notion pages (e.g., prompt packs, frameworks with storytelling). It then crafts a LinkedIn post emphasizing personal hooks (\"I grew my LinkedIn to 10k followers using Claude Code\") and numbers for performance. Publishes via Blot (pre-configured MCP). Records a browser-scroll video of the Notion page, burns in branded overlay. Stores post ID, hook type, text, angle in Notion's tracking database.",[100,3924,3925,3928],{},[3792,3926,3927],{},"Metrics Scraper (10 AM)",": Apify actor (high-rated LinkedIn scraper) fetches engagement for recent posts (initially seeded with Rogoff's last 20). Updates Notion with likes, comments, shares, impressions.",[100,3930,3931,3934],{},[3792,3932,3933],{},"Weekly Autoresearch (Sundays, tunable to 2-3x\u002Fweek)",": Analyzes Notion data for patterns in hooks, line length, format, post length, content type, angle. Rewrites strategy (e.g., favor I-statements with metrics if they outperform). Feeds improved prompts back into daily pipeline.",[22,3936,3937],{},"Key integrations:",[97,3939,3940,3946,3952,3958],{},[100,3941,3942,3945],{},[3792,3943,3944],{},"Notion",": Lead magnets, post storage, results DB (auto-created by Claude).",[100,3947,3948,3951],{},[3792,3949,3950],{},"Blot",": Autopublish posts.",[100,3953,3954,3957],{},[3792,3955,3956],{},"Apify",": Async actor run + dataset items endpoint for metrics JSON.",[100,3959,3960,3963],{},[3792,3961,3962],{},"GitHub",": Repo for code\u002Ffiles, secrets (Anthropic API, Apify token, Notion, Blot), workflows.",[22,3965,3966],{},"Claude Code handled font addition (Gotham from library), video via built-in browser. Seeding used real examples: Rogoff's top posts (e.g., 25k impressions) as MD files, plus saved high-performers, plus audience info MD (pain points: expertise sans visibility).",[22,3968,3969],{},"Tradeoffs surfaced: Weekly research conservative for data accumulation (faster risks noisy signals); Apify cheap but external cost vs. free Claude scraping (less reliable). Agent teams parallelized but token-heavy.",[22,3971,3972],{},"\"Auto research is taking over the web right now. Basically, all it is is this self-improving loop that this guy Carpathy created essentially for machine learning, but now people are adapting it to all sorts of other use cases.\" – Explains the core loop: generate → measure → analyze → iterate, adapted from ML evals to content.",[17,3974,3976],{"id":3975},"build-process-plan-execute-debug-with-claude","Build Process: Plan, Execute, Debug with Claude",[22,3978,3979,3980,3983],{},"Started in Antigravity: ",[3787,3981,3982],{},"\u002Fplan"," mode for brain-dump (lead magnet skill + autoresearch + Notion + Apify + video). Fed GitHub repo link, example MP4, high-perform post MDs. Claude output a thorough plan unprompted: daily gen\u002Fscrape, weekly rewrite. Refined via chat: audience MD, hypothesis (personal hooks + numbers), agent team yes.",[22,3985,3986],{},"Execution: 10-min agent build pushed full codebase to GitHub (Python-heavy, TypeScript optional). Added secrets manually (Anthropic, Apify, etc.). Tested workflows, iterated on errors.",[22,3988,3989],{},"Debugging chain:",[97,3991,3992,3995,3998],{},[100,3993,3994],{},"Initial error (workflow permissions): Copied log → Claude diagnosed\u002Ffixed all three workflows.",[100,3996,3997],{},"No initial metrics: Seeded with 20-post scrape.",[100,3999,4000],{},"Scraper mismatch: Specified Apify actor ID, sample JSON response.",[22,4002,4003],{},"\"Troubleshooting is 90% of the job. You have to get comfortable spending a little bit of time asking the right questions and working with Claude to basically cover the last 5 to 10% of the project.\" – Rogoff on the reality of AI builds, emphasizing iterative prompting over one-shot perfection.",[22,4005,4006],{},"Claude's plan quote (paraphrased in voice but verbatim intent): \"Build a fully automated, self-improving LinkedIn lead magnet system that runs daily on GitHub actions. Each day it generates a lead magnet plus a LinkedIn post, publishes via Blotato, creates a six to seven second notion scroll video... tracks engagement via Amplify, and runs a weekly auto research loop.\"",[17,4008,4010],{"id":4009},"results-and-early-signals","Results and Early Signals",[22,4012,4013],{},"Post-build: Notion DB auto-populated. Pipelines ran successfully after fixes. System primed with Rogoff's historical data (e.g., hooks, impressions). No live metrics yet (transcript cuts mid-seed), but loop positions for compounding: Poor hooks dropped, winners amplified.",[22,4015,4016],{},"Business impact projected: More consistent high-impression posts (target 90k+) → amplified social proof → agency leads + Buildroom growth. Cost: Negligible (Apify \u003C$5\u002Fmonth initially).",[22,4018,4019],{},"\"Better content on LinkedIn creates more social proof for me, which leads to more leads for my business, which then leads to more social proof for me, which leads to more leads for my business.\" – Ties content directly to flywheel of proof → leads → proof.",[17,4021,95],{"id":94},[97,4023,4024,4030,4033,4036,4039,4042,4045,4048],{},[100,4025,4026,4027,4029],{},"Use Claude Code's ",[3787,4028,3982],{}," mode + real examples (MD posts, MP4 demos, audience MD) to bootstrap complex systems; agent teams for parallelism despite token cost.",[100,4031,4032],{},"Schedule via GitHub Actions with secrets; Apify for cheap, structured scraping (specify actor + sample JSON).",[100,4034,4035],{},"Seed autoresearch with historical data (20+ posts) for faster convergence; track specifics: hooks, lengths, formats, angles.",[100,4037,4038],{},"Hypothesis-driven starts (e.g., I-statements + numbers) + Reddit trends for relevance; tune research frequency (2-3x\u002Fweek post-seed).",[100,4040,4041],{},"Debug by pasting full errors into Claude—expect 90% troubleshooting; fixes often cascade across workflows.",[100,4043,4044],{},"Chain tools orthogonally: Notion (storage), Blot (publish), browser (video), for end-to-end without custom infra.",[100,4046,4047],{},"Personalize: Feed audience pains\u002Fhopes; test scroll videos at 6-7s with branded overlays for engagement.",[100,4049,4050],{},"Measure everything in Notion DB upfront; let loop rewrite prompts autonomously.",{"title":128,"searchDepth":129,"depth":129,"links":4052},[4053,4054,4055,4056,4057],{"id":3896,"depth":129,"text":3897},{"id":3909,"depth":129,"text":3910},{"id":3975,"depth":129,"text":3976},{"id":4009,"depth":129,"text":4010},{"id":94,"depth":129,"text":95},[138],"The #1 community for building a highly-profitable personal brand with AI and Claude Code.\n👉 https:\u002F\u002Fwww.skool.com\u002Fbuildroom\u002F\n\nSummary ⤵️\nI built a self-improving LinkedIn content system using Claude Code and Karpathy's Autoresearch — and it publishes lead magnets, writes posts, records scroll videos, and gets smarter every week. Automatically.\n\n⏱️ Timestamps\n00:00 - Introduction: Claude Code Changes Everything\n01:27 - Who Is Duncan Rogoff?\n01:57 - How LinkedIn Content Drives Business\n02:28 - What Is Karpathy's Autoresearch?\n03:05 - Building a Self-Improving Content System\n04:43 - Setting Up Claude Code in Antigravity\n05:20 - How to Use Claude Code Plan Mode\n07:24 - Feeding Claude Code Real Post Examples\n09:06 - Claude Code Builds the Full Plan\n12:22 - Building GitHub Actions\n14:06 - Setting Up the Apify LinkedIn Scraper\n16:00 - Running the Daily LinkedIn Pipeline\n16:48 - How to Debug Errors With Claude Code\n18:26 - Seeding the Auto Research Database\n19:15 - Autoresearch Finds What Actually Works",{},"\u002Fsummaries\u002F7ad97a9dc97bdeb8-self-improving-linkedin-pipeline-with-claude-code-summary","2026-04-06 14:45:01","2026-04-06 16:42:51",{"title":3886,"description":4059},{"loc":4061},"7ad97a9dc97bdeb8","Duncan Rogoff | AI Automation","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CtB4HP7kHyw","summaries\u002F7ad97a9dc97bdeb8-self-improving-linkedin-pipeline-with-claude-code--summary",[157,4071,156,159],"llm","Duncan Rogoff uses Claude Code to build a daily automated system that generates lead magnets, LinkedIn posts with scroll videos, publishes via Blot, scrapes metrics with Apify, and applies Karpathy's autoresearch loop to iteratively boost performance—all running on GitHub Actions.",[159],"ZFLT70BCiDNTuiQNAwT18k5bGLrNsvXeHgH2W_iHDAk",{"id":4076,"title":4077,"ai":4078,"body":4083,"categories":4157,"created_at":139,"date_modified":139,"description":4158,"extension":141,"faq":139,"featured":142,"kicker_label":139,"meta":4159,"navigation":144,"path":4160,"published_at":4161,"question":139,"scraped_at":4162,"seo":4163,"sitemap":4164,"source_id":4165,"source_name":4166,"source_type":152,"source_url":4167,"stem":4168,"tags":4169,"thumbnail_url":139,"tldr":4170,"tweet":139,"unknown_tags":4171,"__hash__":4172},"summaries\u002Fsummaries\u002Fb63d74ae51ee29da-persist-ai-agent-memory-with-adk-sessions-profiles-summary.md","Persist AI Agent Memory with ADK Sessions & Profiles",{"provider":7,"model":8,"input_tokens":4079,"output_tokens":4080,"processing_time_ms":4081,"cost_usd":4082},4693,1273,19961,0.00155245,{"type":14,"value":4084,"toc":4152},[4085,4089,4092,4096,4099,4113,4116,4142,4145,4149],[17,4086,4088],{"id":4087},"durable-chats-via-pluggable-session-services","Durable Chats via Pluggable Session Services",[22,4090,4091],{},"ADK agents lose in-memory session data on app restarts, wiping conversation history and state. Fix this by swapping InMemorySessionService (fast but ephemeral) for DatabaseSessionService, which persists user messages, agent replies, and state changes to a database file or Postgres. Agent logic stays unchanged—use the helper get_or_create_session: it fetches existing sessions by ID to resume or creates new ones. Same session ID post-restart loads full history and latest state, enabling seamless continuation. ADK also supports managed Vertex AI Session Service for cloud persistence, covered in future episodes on memory banks.",[17,4093,4095],{"id":4094},"long-term-personalization-with-user-profile-tools","Long-Term Personalization with User Profile Tools",[22,4097,4098],{},"Persistent sessions resume ongoing chats but fail for new sessions (different IDs). Store key user facts in a simple database table: one row per user ID and preference key like dietary, favorite_thing, or transport_mode—keep it structured and small for easy curation. Equip the agent with two tools tied to user ID context:",[97,4100,4101,4107],{},[100,4102,4103,4106],{},[3792,4104,4105],{},"recall_user_preference",": Reads all saved preferences for the user.",[100,4108,4109,4112],{},[3792,4110,4111],{},"save_user_preference",": Inserts or updates a key-value pair, returning success confirmation.",[22,4114,4115],{},"Embed instructions in agent prompts:",[3915,4117,4118,4124,4130,4136],{},[100,4119,4120,4123],{},[3792,4121,4122],{},"Recall first",": Call recall at conversation start.",[100,4125,4126,4129],{},[3792,4127,4128],{},"Personalize and plan",": Use retrieved data to tailor responses.",[100,4131,4132,4135],{},[3792,4133,4134],{},"Present and learn",": After planning, ask about new facts to save.",[100,4137,4138,4141],{},[3792,4139,4140],{},"Save last",": Call save before ending if new info provided.",[22,4143,4144],{},"This setup personalizes even brand-new chats weeks later without relying on transcripts.",[17,4146,4148],{"id":4147},"demo-validates-cross-session-recall","Demo Validates Cross-Session Recall",[22,4150,4151],{},"In practice, first-time user asks for trip planning: agent recalls nothing, proposes plan, asks for preferences. User specifies \"vegetarian\"; agent saves it successfully. App restart clears RAM but loads session from disk and profile from table. Existing chat resumes mid-flow. New chat (fresh ID) triggers recall, finds dietary=vegetarian, and instantly personalizes the trip plan. Source code via provided links (e.g., goo.gle\u002Fagentmemorylab) shows full integration. Next steps extend to memory banks archiving full conversations with semantic search across text, images, audio, video.",{"title":128,"searchDepth":129,"depth":129,"links":4153},[4154,4155,4156],{"id":4087,"depth":129,"text":4088},{"id":4094,"depth":129,"text":4095},{"id":4147,"depth":129,"text":4148},[],"Lab → https:\u002F\u002Fgoo.gle\u002Fagentmemorylab\nADK doc → https:\u002F\u002Fgoo.gle\u002F4vhpjR1 \nADK session → https:\u002F\u002Fgoo.gle\u002F4vfEBpd \n\nWant your AI agent to remember conversations across sessions and restarts? In this tutorial, you'll learn how to add persistent memory to your agent using Google's Agent Development Kit (ADK), including database-backed sessions and a user profile store.\n\nChapters:\n0:00 - Intro\n1:14 - How to make the chat durable with DatabaseSessionService\n3:02 - How to build a user profile store with recall and save tools\n4:43 - Demo \n6:14 - Recap\n\nWatch more AI agent crash course → https:\u002F\u002Fgoo.gle\u002FAIforBeginners\n🔔 Subscribe to Google Cloud Tech → https:\u002F\u002Fgoo.gle\u002FGoogleCloudTech\n\n#GoogleCloud #AIAgents #ADK\n\nSpeakers: Annie Wang\nProducts Mentioned: Agent Development Kit, Gemini",{},"\u002Fsummaries\u002Fb63d74ae51ee29da-persist-ai-agent-memory-with-adk-sessions-profiles-summary","2026-04-08 15:56:42","2026-04-10 03:09:44",{"title":4077,"description":4158},{"loc":4160},"b63d74ae51ee29da","Google Cloud Tech","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HDqzJJhZsxw","summaries\u002Fb63d74ae51ee29da-persist-ai-agent-memory-with-adk-sessions-profiles-summary",[156,159],"Replace ADK's InMemorySessionService with DatabaseSessionService to save chats across restarts; add recall\u002Fsave tools for user preferences in new sessions.",[159],"_GoSQPRcJ-cneT-w7cu0h_8Z01M4Fz9UXRZU9lxKsTo",{"id":4174,"title":4175,"ai":4176,"body":4181,"categories":4218,"created_at":139,"date_modified":139,"description":4219,"extension":141,"faq":139,"featured":142,"kicker_label":139,"meta":4220,"navigation":144,"path":4221,"published_at":4222,"question":139,"scraped_at":4223,"seo":4224,"sitemap":4225,"source_id":4226,"source_name":4227,"source_type":152,"source_url":4228,"stem":4229,"tags":4230,"thumbnail_url":139,"tldr":4231,"tweet":139,"unknown_tags":4232,"__hash__":4233},"summaries\u002Fsummaries\u002Fcdfe77182714c38a-autoagent-optimizes-harnesses-like-karpathy-s-auto-summary.md","AutoAgent Optimizes Harnesses Like Karpathy's Auto-Research",{"provider":7,"model":8,"input_tokens":4177,"output_tokens":4178,"processing_time_ms":4179,"cost_usd":4180},4800,1331,10221,0.00133465,{"type":14,"value":4182,"toc":4213},[4183,4187,4190,4193,4197,4200,4203,4207,4210],[17,4184,4186],{"id":4185},"core-self-improvement-loop-edit-eval-iterate-overnight","Core Self-Improvement Loop: Edit, Eval, Iterate Overnight",[22,4188,4189],{},"Karpathy's auto-research uses a simple setup with one GPU and 5-minute training runs: fix data prep\u002Ftokenizer (prep.py), let an agent edit training code (train.py) for model, loop, hyperparameters, then evaluate per human instructions in program.md. If metrics improve, commit changes; else revert. Humans \"program in natural language\" via program.md, agent handles code. Run overnight for real gains without manual coding.",[22,4191,4192],{},"AutoAgent applies identical loop to agent harnesses instead of ML training: meta-agent edits task agent's prompts, tools, orchestration (agent.py), runs evals on benchmarks via adapters, commits improvements based on results and reasoning traces. Starts with minimal bash tool; discovers domain-specific logic autonomously.",[17,4194,4196],{"id":4195},"architecture-enables-parallel-domain-agnostic-optimization","Architecture Enables Parallel, Domain-Agnostic Optimization",[22,4198,4199],{},"Split into meta-agent (orchestrates iterations, spins thousands of parallel sandboxes) and task agent (executes domain tasks). Connects to any benchmark (e.g., SpreadsheetBench, TerminalBench) for verification. Same files as auto-research: program.md for human guidance on goals\u002Favoidances, agent.py as editable target.",[22,4201,4202],{},"Simplicity mirrors Karpathy: no complex infra needed. Meta-agent reads traces\u002Fresults post-sandbox runs, decides keeps\u002Freverts, builds specialized tooling\u002Fverification\u002Forchestration nobody coded manually.",[17,4204,4206],{"id":4205},"benchmark-gains-and-harness-engineering-trade-offs","Benchmark Gains and Harness Engineering Trade-offs",[22,4208,4209],{},"On SpreadsheetBench\u002FTerminalBench, iterations show harness improving: better prompts\u002Ftools yield higher scores, compounding overnight. Enables cheaper, specialized agents per domain\u002Fworkflow vs. monolithic harnesses.",[22,4211,4212],{},"Harness optimization critical because domains need tailored prompts\u002Ftools (e.g., spreadsheets vs. terminals), requiring domain+model expertise. Companies gain from stack-specific harnesses running smaller models. Future: domain experts write program.md, meta-agents auto-engineer harnesses—like AI now writes code—define success, return in 24h with optimized setup.",{"title":128,"searchDepth":129,"depth":129,"links":4214},[4215,4216,4217],{"id":4185,"depth":129,"text":4186},{"id":4195,"depth":129,"text":4196},{"id":4205,"depth":129,"text":4206},[172],"Auto Agent: Self-Improving AI Harnesses Inspired by Karpathy’s Auto-Research Loop\n\nThe video explains self-improving agents and highlights Kevin Guo’s Auto Agent project as an extension of Andrej Karpathy’s auto-research idea. Auto-research lets an AI agent iteratively edit training code (e.g., train.py) under a small LLM training setup, run short trainings, evaluate results, and keep or discard changes based on improvement, guided by human-written instructions in program.md. Auto Agent applies the same loop to a different target: optimizing the agent harness itself (prompts, tools, orchestration) rather than ML training code. It uses a meta-agent and a task agent, connects to benchmarks via an adapter, and runs many parallel sandboxes to evaluate iterations using results and reasoning traces. Examples include SpreadsheetBench and TerminalBench, illustrating harness improvements and the broader implications for domain-specific workflows and cheaper, specialized agent setups.\n\nLinks;\nhttps:\u002F\u002Fx.com\u002Fkarpathy\u002Fstatus\u002F2030371219518931079\nhttps:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fautoresearch\nhttps:\u002F\u002Fx.com\u002Fkevingu\u002Fstatus\u002F2039843234760073341\nhttps:\u002F\u002Fgithub.com\u002Fkevinrgu\u002Fautoagent\u002Fblob\u002Fmain\u002Fprogram.md\n\n00:00 Self Improving Agents\n00:33 Auto Research Recap\n01:25 Why Simplicity Worked\n02:22 Auto Agent Architecture\n03:20 Benchmarks And Results\n03:52 Why Harness Optimization Matters\n04:36 Future Of Meta Agents\n05:01 Wrap Up",{},"\u002Fsummaries\u002Fcdfe77182714c38a-autoagent-optimizes-harnesses-like-karpathy-s-auto-summary","2026-04-04 20:07:15","2026-04-05 16:14:38",{"title":4175,"description":4219},{"loc":4221},"cdfe77182714c38a","Developers Digest","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=RoaPvj9Ovug","summaries\u002Fcdfe77182714c38a-autoagent-optimizes-harnesses-like-karpathy-s-auto-summary",[156,159],"Extend Karpathy's auto-research loop—edit code, run 5-min evals, keep improvements—to agent harnesses (prompts\u002Ftools) via meta-agents, yielding domain-specific agents overnight on benchmarks like SpreadsheetBench.",[159],"1x_fyw1usy6zFyOx11771dm61WSLeiQQ-1FNGvFFXfg"]