I built an AI company to save my open source project

Overall reception

• Many commenters enjoyed the article and praised the perseverance, storytelling, and focus on “real” AI that optimizes operations instead of generating text.
• Several people had successfully used the predecessor (OptaPlanner) in production and expressed intent to use Timefold in future projects.

What Timefold is (constraint solver / “AI”)

• Commenters explain it as a constraint solver / operations research tool, not a typical ML model.
• It supports hard, soft, and now “medium” constraints to reflect must-have, nice-to-have, and “unassigned work” penalties.
• Under the hood it uses meta-heuristics such as simulated annealing, tabu search, and especially Late Acceptance; base algorithms are described as a small fraction of the total work.
• There is debate about what counts as “AI”; some view rule-based / OR systems as classic AI (GOFAI), others note the label is now partly marketing-driven.

Modeling constraints & usability

• Multiple people note that the hardest part is translating messy business logic (dates, skills, exceptions) into constraints.
• Timefold’s “constraint streams” are described as more human-readable than low-level MILP formulations, but the learning curve is still seen as steep.
• One user spent 40+ hours to build an MVP for employee scheduling and felt documentation lacks a clear mental model plus many end‑to‑end real-world examples.
• The team acknowledges past pain points (e.g., a removed project-scheduling example) and emphasizes new quickstarts, REST APIs (field service routing, shift scheduling), and educational material.

Vehicle routing & distance matrices

• A side thread covers vehicle routing: SaaS distance matrices (e.g., Google) are considered expensive at scale.
• Alternatives mentioned include straight-line distances, offline OpenStreetMap routers, OSRM, GraphHopper, and custom caching / precomputation.
• Timefold’s own routing APIs sit atop OSRM or other providers with caching and incremental requests.

Adoption challenges & human factors

• Several commenters confirm that many hospitals, logistics firms, and others still schedule manually despite decades-old algorithms.
• Reasons cited: lack of in-house OR expertise, failure-prone and expensive projects, messy real-world constraints (e.g., snow, narrow roads), and resistance from experienced planners.
• Successful deployments require involving domain experts, treating the solver as “assistant” rather than replacement, and supporting real-time replanning and manual overrides.

Startup economics and open-source business

• A long subthread debates founder compensation: early founders often live off savings and later take below-market salaries, with equity as primary compensation.
• Some see this as necessary “skin in the game”; others view it as undue leverage and a path to personal bankruptcy.
• There is concern about open-source “moats” and whether a larger player could simply host Timefold; commenters suggest the hosted models, APIs, and accumulated expertise may be the defensible layer.

Red Hat / IBM and product evolution

• Some lament Red Hat/IBM killing or sidelining products (including OptaPlanner), and describe OptaPlanner as powerful but difficult and “consulting-heavy.”
• The Timefold team responds that Timefold is designed to be easier, with higher-level abstractions and REST APIs, but acknowledges not all problems will ever be trivial.