# Next Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/next
> Markdown URL: https://aitinkerers.org/technologies/next.md
> Technology record last updated: 2026-02-23T02:17:58Z
> Generated: 2026-08-26T12:20:08Z

Next.js is the full-stack React framework: it delivers high-performance web applications via hybrid rendering and powerful, Rust-based tooling.

This is the React Framework for production: Next.js enables you to build full-stack web applications with zero configuration and maximum efficiency. It supports a hybrid rendering approach (Server-Side Rendering, Static Site Generation, and Incremental Static Regeneration) for optimal speed and SEO performance. Key features include React Server Components, Server Actions for running server code directly, and the App Router for advanced routing and nested layouts. Developed by Vercel, it leverages Rust-based tools like Turbopack and the Speedy Web Compiler for the fastest possible builds and a superior developer experience.

- Official technology site: https://nextjs.org/
- Public AI Tinkerers demos and talks: 185
- Result page: 1 of 8

## Recent Public Talks and Demos

### [Selling AI Agents With AI Agents](https://boston.aitinkerers.org/talks/rsvp_CoVeIXBfmhM)

I sell an AI quoting agent to screen printing shops, and I am a solo founder building around a day job, so the constraint is hours, not ideas. I built an outbound GTM engine that runs on a Mac Mini on my desk and works the pipeline while I am at work: weekly lead sourcing and qualification against a narrow ICP (owner-operated shops with an automatic press and an existing quote form), personalized first-touch outreach, revival sequences for dead leads, cadenced follow-up for warm leads, and a Slack command center that flags replies, requests approvals, and tees up the one thing I refuse to automate: the phone call. The product being sold follows the same pattern. The quoter reads messy inbound requests with an LLM, the shop's own deterministic pricing rules calculate every number, and it flips inbound to outbound by chasing stale quotes automatically.

- Event context: Back from Summer: AI GTM Builders — 2026-09-03 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_CoVeIXBfmhM

### [Your BD Pipeline Is A Good First Autonomous Agent](https://boston.aitinkerers.org/talks/rsvp_Vjy6qYY7UX0)

An end-to-end BD pipeline that runs itself on a weekly cron: it sources leads, researches them, drafts the outreach, and emails me the two or three things to do this week. Sourcing → research → draft → nudge, with a lightweight CRM to run it from. One rule holds everywhere: the system researches and drafts; a human always sends. Why this is worth five minutes of a GTM room’s time: almost every agent demo shows one primitive at a time — here’s tool use, here’s a cron, here’s memory. That leaves you knowing the parts and not the shape. BD is a workflow everyone in this room already has opinions about, which makes it a good place to see the whole surface area doing real work at once. The demo is a guided tour of that: I trace one target end to end, and name the primitive earning its keep at each step. The tour, following one company through the system: It gets found. The Opportunity Radar treats my ICP as a graph rather than a list. It starts from private-equity sponsors active in my space and walks outward — a locked sub-agent maps each fund’s portfolio in parallel threads, and qualified portfolio companies auto-feed the pipeline. The frontier of unvisited sponsors lives in a memory store, so the walk resumes each Monday instead of cold-starting. Ownership structure is what makes a buyer resemble my last buyer, so the graph edge qualifies better than a keyword match. It gets carded. The Radar pushes the target into my CRM over MCP — the CRM is itself an MCP server, so the agents and I drive the same pipeline through the same tools. It gets researched. A graded session produces a seven-section pre-call brief off the live web. The run ships with a rubric; the platform scores the deliverable and sends the agent back to revise before I see it. The verdict lands on the card. It gets formatted. Skills load the output contract on demand — including a bundled validate_blocks.py the agent runs against its own draft so the CRM can parse the people and portfolio blocks. It gets drafted. A nightly cron drafts outreach from a compiled brand pack. Secrets come from a vault injected at egress — the agents never see a key. It stops. The draft sits at the gate. Nothing sends. Pipeline status is never machine-written. What I’ll show live: the Monday-morning board with fit scores and grader verdicts that arrived without me; one target’s drawer opened — why-now, signals, key people, dossier, draft; the sweep receipt showing what the Radar covered and where it stopped; a rubric and the revision it forced; and the eval scorecard where a prompt change shows up as a delta against a pinned baseline.

- Event context: Back from Summer: AI GTM Builders — 2026-09-03 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_Vjy6qYY7UX0

### [Measuring velocity direction in AI Era](https://islamabad-rawalpindi.aitinkerers.org/talks/rsvp_a5xgW0Yy5VY)

Momentum; A velocity measuring tool that helps in driving teams in right direction with the right velocity in AI Era. ## THE DEMO A real internal system that we're using and still building to measure velocity, direction and overall organization's momentum. We'll talk about how each matric is being captured, how multiple matrices are complimenting each other and how various team roles i.e. admin, team lead or individual member can read them.

- Event context: AI Tinkerers Islamabad - August 15, 2026 — 2026-08-15 — Islamabad Rawalpindi
- Public talk page: https://islamabad-rawalpindi.aitinkerers.org/talks/rsvp_a5xgW0Yy5VY

### [Pop the Hood on an AI Agent: Loop Engineering on Open Models](https://raleigh.aitinkerers.org/talks/rsvp_lGEh5nJoRO8)

Back in 2022 I did this the hard way: topic modeling in R on data pulled from the then-free Twitter API, to help a company decide which LMS to invest in based on what users and buyers were actually saying. Now, with agents and loop engineering, I wanted to see if I could build something similar on today's tools, and break down what loop engineering actually is while doing it. So I built a competitor-listening agent you can run weekly. Point it at your product and its rivals and it listens across open platforms, then drafts a brief on where you're winning, losing, and the gaps to attack, on open models with no API keys needed to run it. But the point of the demo is what's inside it. When you use an agent in Claude, Copilot, or OpenAI, a loop is running that you never see. I rebuilt that same loop by hand, on open models, so you can watch every part of what an agent is actually doing under the hood. Live, I'll run it in a Colab and walk the loop step by step: gather from open sources, do the cheap NLP work (sentiment, topic modeling) with free, validated tools, and reserve a single open-model call for the actual reasoning, which is reading the signal and naming the gap. Then it drafts the brief, proposes what to watch next, and I approve to widen the following week's listening. You'll see the code, the guardrails, and the repo (a runnable demo plus a fill-in template). It's a live, Colab-fragile build and I'll embrace the rough edges.

- Event context: AI Tinkerers Raleigh Meetup — August 12, 2026 — 2026-08-12 — Raleigh
- Public talk page: https://raleigh.aitinkerers.org/talks/rsvp_lGEh5nJoRO8

### [Converting free Image generator into a paid AI media studio](https://valencia.aitinkerers.org/talks/rsvp_DQaYLcNOM3Y)

ClipMode is a live AI media studio (consistent-face photo/video). The GTM project is the full acquisition → paid conversion system: free generators → trained identity → Premium/credits. Acquisition channels: - Affiliates payouts - Newsletter + email - Blog / SEO content - Paid ads - Influencers (TikTok, YouTube)

- Event context: GTM Teardown August — AI Tinkerers Valencia — 2026-08-11 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_DQaYLcNOM3Y

### [SEO Delphi / AI SEO Agents](https://seattle.aitinkerers.org/talks/rsvp_SABLX9RPvCQ)

SEO Delphi: an autonomous SEO and growth engine for advisor.guide, a financial advisor directory built on public SEC Form ADV data (300k+ advisor profiles). It's a fleet of ~20 Vercel cron jobs plus Claude agents, split into layers: (1) data sync pulls SEC/Form ADV, 13F, and AUM data into Postgres; (2) sensing reads Search Console daily, scrapes Google ranks vs competitors (Wealthtender, SmartAsset, WiserAdvisor) via Serper, and mines AI-citation gaps where ChatGPT/Perplexity cite competitors but not us; (3) deciding ranks "what to write today" from Reddit/news/SpyFu signals, finds the single biggest funnel leak, and logs every shipped change to an experiments ledger so a later cron measures it; (4) acting sends claim-outreach email to unclaimed advisors getting profile views, and drives agent-written content through a scored writing rubric; (5) reporting posts everything to Slack as the "seo-delphi" bot, with a morning pulse split into "humans need to do" vs "what the agents did." I'll show live: the cron code, the Slack feed, real Search Console and rank data, and the experiments ledger.

- Event context: GTMxAI Engineering July - Seattle Tech Week — 2026-07-30 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_SABLX9RPvCQ

### [My Coworkers Are Agents: Running a GTM Consultancy Where Slack-Native AI Workers Build, Monitor, and Self-Heal Client Automations](https://seattle.aitinkerers.org/talks/rsvp_VkekCRBY8TQ)

I built a team of AI "employees" — GTM Engineering agents — that run go-to-market operations for real clients at my consultancy, Tarka. Each agent lives in Slack (one channel per client), and end-to-end it builds, operates, monitors, and auto-recovers the client's GTM stack: prospecting in Deepline → CRM in Attio/Clarify → outbound email in Instantly → LinkedIn outreach via allman.sh, all orchestrated through n8n workflows. You message the agent in Slack like a coworker — "pipeline for this ICP is dry" — and it ships the automation, watches it run, and fixes it when a step breaks. Live, I'll show: (1) an agent taking a Slack request and standing up / repairing an n8n workflow across the GTM tools; (2) the architecture of the platform underneath it (ergoly) — how each agent is an isolated worker bound to a single client; and (3) the monitoring + auto-recovery loop that lets these agents run unattended. I'll pull up the real system: the Slack control plane, the worker sandboxes, and the four-tier memory + credential-injection design that keeps every client's data and secrets separate.

- Event context: GTMxAI Engineering July - Seattle Tech Week — 2026-07-30 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_VkekCRBY8TQ

### [Multi-Pass Building Defect Detection: Getting a VLM to Find Facade Defects for Visual Inspections](https://dc.aitinkerers.org/talks/rsvp_3zzrUv1SHxo)

We built an AI system that turns raw building inspection photos into annotated defect reports, detecting 15 distinct pathologies (building cracks, sealant degradation, brick spalling, mortar erosion, steel corrosion, and so on) with accurate bounding boxes across facade inspections. I'll show the architecture of how we've setup the inspection image processing pipeline: the multi-pass detection architecture, the model training process, the defect annotation catalog, the eval harness we use to verify accuracy on new datasets & catch regressions. And why this was our selected way to set it up. I'll walk through the examples of real reports going from photo dump to structured outputs (where the model gets it right and where it still needs a human check).

- Event context: AI Tinkerers DC Metro - Arlington: July 23rd Meetup — 2026-07-23 — DC
- Public talk page: https://dc.aitinkerers.org/talks/rsvp_3zzrUv1SHxo

### [Researching agent memory](https://columbus.aitinkerers.org/talks/rsvp_9_U1zLJVobc)

What started as a conversation to understand agent memory frameworks turned into a directory website and a fully autonomous self learning researcher and reporter.

- Event context: AI Tinkerers - Columbus July Meetup — 2026-07-06 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_9_U1zLJVobc

### [Building Brushi: A Local VLM Agent That Understands Kids’ Drawings](https://toronto.aitinkerers.org/talks/rsvp_duZsN9qwZvs)

Brushi is a kids coloring and drawing app with a local multimodal AI companion that can observe what a child is creating and respond through a small, bounded interaction loop. The project explores a question I have been thinking about in my recent work on edge AI: small VLMs and LLMs should not be treated as weaker versions of large cloud models. Like children are not simply “small adults,” edge models behave differently because the system around them has to provide structure, grounding, and feedback. In the live demo, I’ll show the working Brushi app, the local VLM/agentic architecture, and the event loop that connects the child’s drawing activity to the AI mascot’s reactions. The demo will focus on the technical workflow: how drawing events are captured, how visual context can be passed to a local VLM, how the LLM layer interprets the learning or creative context, and how the app decides whether to encourage, guide, correct, or stay silent. I’ll show the architecture, app workflow, logs/traces from the interaction loop, and examples of where the system succeeds or fails when trying to understand a child’s drawing.

- Event context: AI Tinkerers Toronto - June 2026 - with Relay Financial x PostHog — 2026-06-30 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_duZsN9qwZvs

### [From AI Prospect Memos to a Scored Warm-Intro Queue](https://poland.aitinkerers.org/talks/rsvp_k0DCDscxWlA)

I built a dogfood workflow on top of graph.one that connects AI-assisted company qualification to a relationship-graph work queue. I'm the founder of graph.one, but this demo is about the internals of the build, not a product pitch. It starts in Prospector: a Node CLI pipeline that creates frozen public-source packets, runs AI-generated prospect memos, validates structured outputs, and stores decisions in append-only JSONL ledgers. The handoff is a qualified-company record. Intro Queue then takes a relationship-graph org-path export with organization identity, relationship owner, optional connector, target person/role, and strength fields. It caches the export in SQLite, collapses duplicate and noisy paths into scored route options, groups selected routes by connector, tracks per-company outcomes, and surfaces fallback routes as a human action queue. In the demo I'll follow one fully synthetic target company from source packet to memo to graph path row to scored route option to failed route/fallback. The queue output is a person-centric ask docket for a human operator; there is no send pipeline. I'll use fully synthetic graph data and redacted schema/log excerpts only.

- Event context: AI Tinkerers Warsaw: GTM Engineering Track — 2026-06-24 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_k0DCDscxWlA

### [Smart flascard](https://nurnberg.aitinkerers.org/talks/rsvp_qt8PZZzucz8)

Smart Flashcard is a vocabulary learning app that helps users turn new words into guided practice and daily study habits. I will demonstrate the live product, including word capture, review sessions, personalized practice, and the learning workflow.

- Event context: AI Tinkerers Nürnberg: June Meetup (Community Hub) — 2026-06-24 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_qt8PZZzucz8

### [WebRTC Powered Agents](https://montreal.aitinkerers.org/talks/rsvp_wR1CDxmD5UI)

A simple live-video WebRTC pipeline you can hook LLM agents and ML models into. I will show code and basic architecture for: - Setting up a WebRTC pipeline. - Conversational agent interactions. (Query against video footage, manipulating app state, etc...) - Promptable DINO-based realtime object detection. - Realtime SAM2 segmentation. - Realtime depth detection.

- Event context: AI Tinkerers Montreal - June Demo Meetup — 2026-06-17 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_wR1CDxmD5UI

### [Why I gave up on agent voting: hard escalation in a 3-reviewer LLM pipeline](https://nyc.aitinkerers.org/talks/rsvp_gIVCOg1tRfI)

Redline is a production-readiness review tool for AI agents. Three specialized LLM reviewers (Engineer, Risk, Business) read an agent transcript against a company's pasted rulebook, mark up the dossier in three colors, and a synthesizer returns a Deploy, Hold, or Kill verdict. Live demo: pasting a real agent dossier into sanjitkangovi.com/redline, watching the three reviewers annotate in parallel, then walking through the deterministic synthesis layer in the backend that decides the final verdict. I'll show the prompt structure for each reviewer role, the JSON contract between reviewer and synthesizer, and the server-side escalation logic firing when a critical Risk finding contradicts the other two reviewers.

- Event context: NY Tech Week Demo Day ft PostHog, Convex, Veris, & HPE — 2026-06-03 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_gIVCOg1tRfI

### [Agentic video editing pipeline](https://montreal.aitinkerers.org/talks/rsvp_5bqTjWicF8s)

After interviewing 50+ video editors, head of marketing, film directors, we found the boring part of the professional video editing job that can be automated. I will demo the main video ingestion workflow: transcode to 480p using ffmpeg, upload that video to GCS, takes some frames and use it for classification, ask gemini to look at the video for video understanding, piping the audio to assemblyai for the transcript, use ffmpeg to segment the video, use gemini embedding on each segment so it is searchable.

- Event context: AI Tinkerers Montreal - May Demo Meetup @ Ubisoft — 2026-05-26 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_5bqTjWicF8s

### [Conversation as a Signal Query Engine - HQIQ Maestro](https://dubai.aitinkerers.org/talks/rsvp_2-90lqKKdGc)

Maestro / Hadi is a voice - first generative UI agent platform — no chat bubble, no text input. A full-screen avatar composes the interface in real time as the user speaks: scenes transition, listings render, comparison views compose themselves, contextual sheets open. The agent IS the frontend. We have shipped Translator Mode — a conversational capability where the same agent enters bilingual interpreter persona mid-session when asked. A realtor speaks English, a guest speaks Arabic (or any of 30+ languages Gemini Live supports), the avatar mediates the exchange in real-time bidirectional audio, then exits gracefully when the realtor asks to return to normal — with all prior property context preserved. Architecture: two function tools (enable_translator_mode / disable_translator_mode) inject interpreter instructions into the agent's chat context via update_chat_ctx, and emit TRANSLATOR_MODE data packets that surface a UI status indicator on the frontend. The LLM detects the trigger phrase naturally — no regex, no state machine. Provider-agnostic: runs on Google DeepMind's Gemini Live API for native audio, or OpenAI Realtime as a fallback. Litmus test: "Would this have been impossible with a chat interface?" Yes. Voice translation IS the conversational cadence chat removes. The translator pattern generalizes — same hook supports accessibility narrators, interview coaches, any role-shaped persona switch. The agent's persona is its capability, not its costume.

- Event context: AI Tinkerers Dubai - May Demo Day — 2026-05-23 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_2-90lqKKdGc

### [Edge Power](https://pereira.aitinkerers.org/talks/rsvp_SRDHdkG1fCM)

I built an IoT-based energy monitoring and optimization platform developed with Next.js, designed to collect, process, and visualize real-time data from distributed devices and edge computing systems. During the live demo, I will showcase the working platform, including the web dashboard, real-time device communication workflow, system architecture, Dockerized deployment structure, source code organization, MQTT/WebSocket data flow, logs, and the integration between embedded devices and the visualization platform. I will also present the repository structure and the overall data processing pipeline running in a functional environment.

- Event context: AI Tinkerers Pereira: Builders Session - Engineering Week Edition — 2026-05-21 — Pereira
- Public talk page: https://pereira.aitinkerers.org/talks/rsvp_SRDHdkG1fCM

### [Presentations Made Easy](https://doha.aitinkerers.org/talks/rsvp_9LeSd0u-leg)

I built an internal slide generator where users add in the slide title and some details about the presentation - and it generates on-brand slides, with built-in interactivity.

- Event context: AI Tinkerers – Doha Inaugural Meetup — 2026-05-18 — Doha
- Public talk page: https://doha.aitinkerers.org/talks/rsvp_9LeSd0u-leg

### [ICM - Interpreted Context Methodology](https://columbus.aitinkerers.org/talks/rsvp__HgYuH0-pAY)

I've built upon Jake Van Clief's Interpretable Context Methodology (ICM) - to write my multi-step agent workflows as folders with markdown inside of them. Skill files to get started are linked.

- Event context: AI Tinkerers - Columbus May Meetup — 2026-05-04 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp__HgYuH0-pAY

### [inteligencia Artificial y creatividad: Dos fronteras cada vez mas juntas](https://pereira.aitinkerers.org/talks/rsvp_z2VSRgVfmfk)

Una herramienta para la escritura de libros, ebooks, biografias impulsada con inteligencia artificial.

- Event context: AI Tinkerers Pereira: Innovando con IA desde el SENA — 2026-04-30 — Pereira
- Public talk page: https://pereira.aitinkerers.org/talks/rsvp_z2VSRgVfmfk

### [Merging Multi Agent Council Delibrations with Dynamic Analytics to Redesign Transit Infrastructure](https://toronto.aitinkerers.org/talks/rsvp_zWQT1Z51PV8)

A sandbox planning tool where you draw new Toronto transit lines and a 6-agent AI council critiques it; with the ability to see real-time changes and analytics for routes and ridership as the line is developed.

- Event context: AI Tinkerers Toronto - April 2026 - hosted by Shopify — 2026-04-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_zWQT1Z51PV8

### [shelf: autoresearch for Shopify storefronts](https://toronto.aitinkerers.org/talks/rsvp_oVI3sYF9QNQ)

shelf is an open-source CLI that applies the autoresearch loop (the same pattern Tobi Lutke used to make Liquid 53% faster) to Shopify product catalogs. Instead of optimizing build time, it optimizes AI discoverability. It generates shopper queries, asks them to real AI agents via their APIs, scores how often your products surface, proposes one atomic catalog change, applies it via Shopify's Admin GraphQL API, re-scores, and keeps or reverts. It runs until you tell it to stop.

- Event context: AI Tinkerers Toronto - April 2026 - hosted by Shopify — 2026-04-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_oVI3sYF9QNQ

### [Supercharging Development with Claude Cowork](https://montreal.aitinkerers.org/talks/rsvp_73dcPJFKniM)

Coluur is a watercolor paint matcher for iOS and web — upload a photo and it returns the exact Sennelier, Daniel Smith, Winsor & Newton, or Holbein tubes (with pigment codes, lightfastness, and transparency ratings) that reproduce every colour in it, plus two-paint mix recipes when no single tube is close enough. Demo plan: I'll upload a reference photo live and walk through the extraction and match results (~30s), then hand the screen to Cowork — Anthropic's desktop agent — to have it pick up a real Notion ticket from the Coluur sprint board and ship a visible change to the running app in under a minute.

- Event context: AI Tinkerers Montreal - April Demo Night — 2026-04-22 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_73dcPJFKniM

### [Building rigorous AI voice-agent assessments for leadership training](https://valencia.aitinkerers.org/talks/rsvp_cVvSCrbV0JE)

A leadership training platform that simulates difficult interactions with AI voice agents and provides AI powered learnings bits and assessments.

- Event context: AI Tinkerers Valencia April Meetup — 2026-04-21 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_cVvSCrbV0JE

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## More Results

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