Working integrations, knowledge tools, a publishing product and further experiments.
AI task intake & calendar integration
Independent projectDeployed projectNatural language requests become validated task and calendar actions, with recovery when an external write fails.
for: Teams coordinating tasks across chat and planning tools
My ownership: Workflow rules, tool integration, operating controls and acceptance, with AI supported implementation.
Inspect tested workflow behaviours →Telegram request→Structured AI plan→Validation→Stored operations→Trello and Calendar
context:independent implementation with operating controls
▸ problem, architecture, tradeoffs▾ problem, architecture, tradeoffs
Delivery and adoption
Deployed for personal coordination. Validation, retries, backups and restore behaviour are part of the operating design.
The problem
Natural language requests need to become consistent actions across multiple tools, including when dates are unclear or a downstream service fails.
Implementation or proposed flow
Built and deployed an independent service using OpenAI structured plans and Pydantic validation. PostgreSQL stores plans and operations. Queries cannot carry write actions, ambiguous dates trigger clarification, and retries reconcile earlier writes. Authenticated intake, health checks, backups and a verified restore support operation. I own workflow rules and acceptance, with coding agents supporting implementation.
The tradeoffs
- Deterministic calendar routines, because mirroring and date sorting have explicit rules and need no model call.
- Persisted operations and reconciliation, because a retry must account for an external write that may already have succeeded.
Outcome
Deployed for personal task coordination, with validated actions and tested recovery after failed operations.
What I would watch next
- Duplicate actions after a retry
- Requests that require clarification
- Backup restoration and failed operation recovery
PythonOpenAIPydanticPostgreSQLTelegramTrelloGoogle CalendarDockerHetzner
AI publishing product | Fantasy football
Preparing for reader launchPrototype in testingOfficial league data becomes a designed publication, with score validation and editorial review before release.
for: Participants who want useful analysis and a readable story from their league data
My ownership: Product scope, data quality, editorial decisions and release acceptance, with AI supported implementation.
Official league data→Score validation→Forecast comparison→AI assisted editorial→Designed magazine
context:testing before reader launch
▸ problem, architecture, tradeoffs▾ problem, architecture, tradeoffs
Delivery and adoption
Latest completed editorial milestone: a 22 page pregame format. Next: reader feedback on usefulness and publication quality.
The problem
Raw scores tell readers what happened, but a useful recurring publication needs reliable numbers, context and an editorial structure.
Implementation or proposed flow
Building a publishing workflow that combines official league results and player data with analysis and a designed magazine. Numerical validation comes before editorial generation. Forecasts are fixed before matches and assessed against outcomes. I own scope, data quality, editorial review and preparation for reader launch, using AI coding agents for implementation.
The tradeoffs
- Verified numbers before generated prose, because a persuasive narrative cannot compensate for an incorrect score.
- Forecasts fixed before results, because evaluation is meaningful only when predictions cannot change after the event.
Outcome
The prototype reconciled 60 historical scores and passed 19 regression tests. Current focus: reader testing, editorial usefulness and a repeatable publication workflow.
What I would watch next
- Score agreement with official results
- Editorial usefulness in reader testing
- Publication preparation time
PythonREST APIsSQLiteAI assisted editorialHTMLPDF
MCP knowledge tools for AI delivery
Independent projectIn useSource retrieval and capture keep research, project decisions and working context available across AI sessions.
for: Builders and operators coordinating work across AI sessions
My ownership: Tool boundaries, retrieval and capture requirements, fixture testing and ongoing use.
Capture→Reviewed knowledge→MCP search→Full source retrieval→Project work
context:used in my own research and project delivery
▸ problem, architecture, tradeoffs▾ problem, architecture, tradeoffs
Delivery and adoption
Four focused MCP tools keep source material and working context accessible across AI sessions.
The problem
Research and decisions are easily lost across separate AI conversations, leading to repeated work and inconsistent assumptions.
Implementation or proposed flow
Built a knowledge environment with custom MCP tools for keyword search, full note retrieval, new captures and recent activity. Inputs are validated, article paths are restricted and earlier captures are preserved. Source material stays separate from operating instructions. I use the environment for ongoing research and delivery.
The tradeoffs
- Small tools with explicit file boundaries, because each operation can be understood and checked.
- Keyword search and full source reads, because this implementation needs inspectable retrieval without claiming a vector search platform.
Outcome
Used in ongoing research, planning and project delivery. Fixture checks cover retrieval, capture preservation and file access boundaries.
What I would watch next
- Whether retrieved sources answer the working question
- Stale or conflicting decisions
- Captures that have not yet been reviewed
ObsidianPythonFastMCPPydanticMCP
Account research to reviewable outreach
Independent projectWorking prototypeThree AI stages connect account enrichment, segmentation and outreach preparation.
for: Sales teams handling repetitive account preparation
My ownership: Business rules, structured handoffs, integration choices and acceptance.
Account→API enrichment→Segmentation→Outreach draft→Human review
context:prototype with explicit review and validation steps
▸ problem, architecture, tradeoffs▾ problem, architecture, tradeoffs
Delivery and adoption
The prototype separates account enrichment, segmentation and drafting. A person reviews outreach before sending.
The problem
Account research, segmentation and outreach drafting are often separate tasks with inconsistent handoffs.
Implementation or proposed flow
Built a CRM prototype where enrichment, segmentation and drafting agents exchange structured outputs. Segmentation uses deal history and engagement; stage outputs and anomaly flags remain inspectable. I own the business rules, integration choices and acceptance. External outreach remains subject to human approval.
The tradeoffs
- Separate stages, because a failed step can be investigated without rerunning the whole workflow.
- Human approval before outreach, because a draft needs a commercial owner before it reaches another person.
Outcome
Working prototype with reviewable stages. No production customer adoption or conversion result is attributed to this independent project.
What I would watch next
- Incorrect account matches
- Draft quality and required edits
- Errors at handoff between stages
OpenAIOllamapandasStreamlitREST APIs
Support routing evaluation
Evaluation harnessWorking prototypeCompare rules and semantic classification before deciding which requests should be automated.
for: Teams deciding where automated routing is appropriate
My ownership: Test cases, expected routes, review thresholds and interpretation of errors.
Authored support cases→Rules and semantic routes→Expected versus actual→Error inspection→Review threshold
context:prototype with explicit review and validation steps
▸ problem, architecture, tradeoffs▾ problem, architecture, tradeoffs
Delivery and adoption
Authored cases make failure patterns inspectable before applying a routing policy to real traffic.
The problem
A routing workflow needs a way to expose incorrect automatic decisions and the requests it sends for review.
Implementation or proposed flow
Built a harness comparing keyword rules with semantic classification on 20 authored support cases. Expected and actual routes and review thresholds remain visible. Failures can be retained as regression cases.
The tradeoffs
- Errors alongside automation coverage, because a higher routing rate is not useful if it hides more incorrect assignments.
Outcome
Working evaluation demonstration. Authored test cases do not establish production accuracy.
What I would watch next
- Wrong automatic decisions
- Review workload
- Coverage of new request categories
RulesSemantic classificationEvaluationReview thresholds
Polywhale | Signal analysis prototype
Independent prototypeA data analysis prototype exploring convergence patterns across participant activity.
▸ how it works▾ how it works
A data analysis prototype exploring convergence patterns across participant activity. Built with AI implementation support.
FastAPIReactSupabaseREST APIs
PolyGod | Market analytics prototype
Independent prototypeA prototype exploring scheduled data collection and structured market analysis.
▸ how it works▾ how it works
A prototype exploring scheduled data collection and structured market analysis. Built with AI implementation support.
SupabaseData pipelinesWorkflow automation
Canvas Pro | AI image tools
Independent prototypeA prototype for working with image generation across model providers.
▸ how it works▾ how it works
A prototype for working with image generation across model providers. Built with AI implementation support.
TypeScriptReactSupabaseAI APIs
Draft AI Ace | Draft decision support
Independent prototypeA fantasy sports prototype exploring player recommendations under a draft clock.
▸ how it works▾ how it works
A fantasy sports prototype exploring player recommendations under a draft clock. Built with AI implementation support.
TypeScriptReactSupabaseAI assistance
Real Cost Finder
Independent prototypeA travel comparison prototype separating language based input interpretation from cost calculations.
▸ how it works▾ how it works
A travel comparison prototype separating language based input interpretation from cost calculations. Built with AI implementation support.
PythonFastAPIGemini APIREST APIs
FootballAnalyzer
Independent prototypeA football data analysis project combining API ingestion, transformations and visual exploration.
▸ how it works▾ how it works
A football data analysis project combining API ingestion, transformations and visual exploration. Built with AI implementation support.
pandasNumPyStreamlitAPI Football