start-here

Keyword

start-here

A deliberately ambiguous trailhead into the Field Notes archive: AI systems, live production, strategic signal detection, semantic search, responsible web work, and the human judgment behind the machinery.

Entries: 9
Latest: July 18, 2026
Type: Keyword

OpenAI Build Week: What Would You Ask Sam?

applied-aiweb-developmentProject: samnavsemantic-searchai-workflowsstart-here 4 min read

OpenAI Build Week: What Would You Ask Sam?

OpenAI emailed me about Build Week with four days left, which meant choosing between several questionable Codex projects and figuring out how to make a meaningful addition to SamNav—an application that was already built, deployed, and working. The answer became “What would you ask Sam?”: a question-first way to navigate Sam Altman’s public writing without pretending to impersonate him. The new system uses SamNav’s existing taxonomy, semantic dossiers, and GPT-5.6 to interpret nuanced questions and plot a grounded route through things he has actually published.

Navigation for the End Times

web-developmentProject: samnavweb-designclassificationanti-extractionnonsensestart-here 6 min read

Navigation for the End Times

SamNav is a tiny independent index for Sam Altman’s blog, built because the posts are worth reading but the navigation is almost comically minimal. It adds search, topics, reading paths, related posts, and an admittedly dry Navigation Severity Score without republishing the original writing. It’s part joke, part web repair: a small Dockerized map for a corner of the internet that needed one.

Expertise Without Permission

culture-and-critiqueapplied-ailabor-and-classcraftautomationanti-extractionnonsensestart-here 9 min read

Expertise Without Permission

A working-class reflection on AI, expertise, and gatekeeping. The piece argues that expertise is real, but access to it has always been shaped by class, credentials, and permission. AI disrupts that arrangement by letting more people turn ideas into practice before institutions decide they are “qualified.” That democratizing potential is real, but complicated: the same tools that weaken old gates are mostly owned by corporations, turning liberation into subscription-based dependency. The essay defends craft while rejecting the fantasy that craft alone protects workers.

A Machine for Cross-Examining My Greed

applied-aiweb-developmentProject: signal-deskresponsible-webai-workflowsdata-architecturenonsensestart-here 6 min read

A Machine for Cross-Examining My Greed

Signal Desk is a local AI-powered investment research desk I built precisely because I don’t know enough about investing to trust my own instincts. It turns scattered curiosity about AI companies, infrastructure, robotics, sensors, and market hype into a slower research ritual: filings, thesis cards, source-linked briefs, recommendation categories, discovery scans, and ghost trades that test ideas without risking real money. It does not give financial advice or place trades. Instead, it acts as a contradiction engine, helping me separate evidence from narrative before I do something financially...

The Ghost Switcher

media-and-productionapplied-aiProject: autonomous-producerlive-productionsystems-designlocal-firstagentic-systemshuman-in-the-loopstart-here 10 min read

The Ghost Switcher

Part three closes the series by moving from prototype to stakes: a real venue-network failure in London where the remote producer was disconnected, the stream dropped, and the online audience was left staring into the void. From that failure, the article argues that Autonomous Producer is not just about smarter switching, but about removing fragile remote-control dependencies from live production. The answer is a local “ghost switcher” that observes the run of show, source states, transcript cues, and device health; recommends actions in shadow mode; logs every decision; moves through human-confirmed switching; and only earns narrow autopilot after repeated evaluation. Its intelligence is not one giant model, but a resilient local control loop built from constrained commands, structured observations, production rules, and the discipline to hold when the evidence is bad.

The Show Has a Spine

media-and-productionapplied-aiProject: autonomous-producerlive-productionconference-techlocal-firstagentic-systemshuman-in-the-loopstart-here 9 min read

The Show Has a Spine

Part two follows the project from portable livestream workflow into live system architecture, opening with a makeshift hotel-room lab in Killarney where OBS, ATEM control, testing sources, and early automation experiments began turning into something more coherent. The central idea is that an autonomous producer should not be one giant AI model “watching” a show, but a local orchestration system that understands the show through a run of show, device state, sampled video, rolling transcripts, source-state signals, and a director policy. This part argues that scientific livestreams are not just sequences of cuts, but sequences of expectations, cues, timing, identities, and drift, and that the real technical challenge is giving the machine a structured point of view about what is happening before asking it to recommend or execute production decisions.

Signal to Strategy: From Scientific Activity to Demand Intelligence

data-and-classificationProject: signal-to-strategybusiness-intelligenceconference-planningclassificationlife-sciencesstart-here 4 min read

Signal to Strategy: From Scientific Activity to Demand Intelligence

Scientific activity is not the same thing as scientific demand. This article argues that publications, grants, registrations, abstracts, and engagement metrics only become strategically useful when interpreted together, classified consistently, and understood in context. Scientific demand intelligence is presented as a decision-support framework for identifying where research momentum is building before it becomes obvious through late-stage operational signals. Rather than replacing expert judgment, it gives organizations a more structured way to recognize weak signals, understand emerging communities, and make better decisions about what to convene, where to invest, and when to act.

Signal to Strategy: Why Scientific Classification is More than a Labeling Exercise

data-and-classificationProject: signal-to-strategybusiness-intelligenceconference-planningdata-architecturesemantic-searchstart-here 4 min read

Signal to Strategy: Why Scientific Classification is More than a Labeling Exercise

Scientific classification is more than tagging content after the fact. This article argues that classification should function as an interpretive layer inside the data architecture, giving messy scientific and operational records a shared vocabulary that makes them comparable, traceable, and strategically useful. Publications, grants, abstracts, investigator activity, and event signals all describe scientific activity from different angles, but without a durable classification layer they remain difficult to connect. By using Snowflake as the analytical backbone, supported by local validation workflows involving Postgres, Qdrant, OpenAlex, embeddings, and structured JSON outputs, the goal is to create an enrichment system that is inspectable enough to trust and flexible enough to refine. In a conference context, that layer can help identify emerging areas, converging communities, and better evidence for planning decisions.

Signal to Strategy: Why Scientific Demand is Difficult to Detect

data-and-classificationProject: signal-to-strategybusiness-intelligenceconference-planningclassificationlife-sciencesstart-here 3 min read

Signal to Strategy: Why Scientific Demand is Difficult to Detect

Scientific demand is difficult to detect because it rarely appears as a single clean metric. By the time it shows up in registrations, abstracts, sponsor interest, or meeting-planning conversations, the most useful window for strategic interpretation may already have passed. This article argues that demand often emerges earlier through weak, distributed signals across publications, grants, collaborations, methods, disease areas, and shifting scientific attention. The challenge is not collecting more data, but building systems that can classify, connect, and interpret those signals in context so organizations can recognize emerging relevance sooner and make better decisions about which communities to convene and where to invest.