About Intent Solutions Learn
Intent Solutions Learn is a practitioner practice for agentic systems that teaches the Intent method through production-shaped work for serious practitioners building agentic systems.
Members define done, test the claim, publish evidence, and sharpen their judgment with a selective peer group.
What Intent Solutions Learn does
Teaches the Intent method
The practice teaches a repeatable sequence: define done, test the claim, publish evidence, and operate what survives review. Practitioners leave with a method they can carry across models, tools, and production environments.
Runs production-shaped practice
Lessons and reviews use the failure modes, constraints, and ownership questions that tutorials often omit. The outcome is stronger implementation judgment before a real system carries customer or operational risk.
Offers role-shaped proof paths
Optional paths focus practice around architecture, building, operations, and governance. Each path gives a practitioner a clearer body of evidence than attendance alone.
Builds a selective peer room
Access begins with work and fit, then continues through practice and contribution. A smaller room protects review quality and gives members useful peers instead of an anonymous course audience.
What makes Intent Solutions Learn different
Evidence is part of the work
Claims are expected to resolve to tests, artifacts, measurements, or a clearly labeled unknown. Review focuses on what the system can demonstrate, not how polished the pitch sounds.
The standard is model-agnostic
The house method applies across Claude, Codex, Grok, and other suitable tools. Members choose a model for the constraint while keeping one shared standard for framing, evidence, and operation.
Access starts with demonstrated work
Applicants show what they are building and why the room fits their next step. This keeps peer density high and gives live review a useful technical baseline.
Practice continues into operation
The work does not stop when a prototype produces an impressive result. Ownership, rollback, observability, and response paths remain part of the implementation standard.
The deeper arena is earned
Inner-circle access follows judgment, contribution, and fit rather than a volume enrollment target. That progression gives experienced practitioners a reason to keep contributing after a lesson ends.
Who uses Intent Solutions Learn
- Software engineers building agentic systems that must survive production failure modes.
- Technical founders who need a testable operating method for AI implementation decisions.
- Platform, DevOps, and SRE practitioners adding ownership, observability, and rollback to AI workflows.
- AI governance and evaluation practitioners who need evidence tied to a defined business outcome.
- Experienced builders seeking live review and a selective peer room rather than a self-paced course catalog.
The team behind Intent Solutions Learn
Jeremy Longshore, Founder and CEO
Jeremy Longshore founded Intent Solutions in Gulf Shores, Alabama, after building production AI systems and tools in the Claude Code ecosystem. He sets the house method and the operating standard that connects framing, implementation, evidence, and ownership.
Opeyemi Ariyo, Co-Founder and CTO
Opeyemi Ariyo is the public technical co-founder of Intent Solutions Learn. He provides technical leadership for the systems and engineering work that support the practitioner practice.
Pablo Perez, Co-Founder and Project Manager
Pablo Perez is the public project-management co-founder of Intent Solutions Learn. He helps turn the practice's technical standard into coordinated work, review, and delivery.
Instruction, operations, and design
Max Sheahan serves as Lead Instructor, Tim Neunzig leads German operations, and Tolulope Ariyo works as Software and Design Engineer. Together they cover instruction, regional operations, software, and design around the founding team.
How the practice started
Intent Solutions Learn grew from the need to teach the same evidence-backed habits used to ship accountable AI systems. The team built a selective practice around live review, production-shaped work, and proof paths instead of a high-volume content library.
How Intent Solutions Learn works
Request access by showing work
Applicants use the request-access form to describe what they are building and the constraint they need to solve. The team evaluates fit for the current practitioner room.
Practice the house method
Members frame the outcome, build against explicit acceptance criteria, and test the claim with evidence. Live review turns gaps into the next bounded practice step.
Publish proof
Optional proof paths organize evidence around architecture, building, operations, or governance. The artifact shows the work and the standard applied to it.
Earn the deeper arena
Judgment, contribution, and fit can lead to the inner circle and deeper peer work. Members work with the instructional and technical team through the platform and the published access channel.
Communication and timing: onboarding begins through the request-access form, with jeremy@intentsolutions.io available as the direct contact route. Initial response time, cohort start timing, and review turnaround are [[RESPONSE_AND_TURNAROUND_TARGET]].
Key facts
| Company Name | Intent Solutions Learn |
|---|---|
| Type | Selective practitioner practice for agentic systems |
| Founded | [[FOUNDING_YEAR]] |
| Founder | Jeremy Longshore |
| Headquarters | Gulf Shores, Alabama |
| Website | learn.intentsolutions.io |
| Core Offering | Live practitioner education in the Intent method with optional proof paths |
| Pricing | [[PRICING_MODEL]] |
| Contract Terms | [[CONTRACT_TERMS]] |
| Services | Practice tracks, live review, peer learning, and optional architecture, build, operations, and governance proof paths |
| Communication | Private learning platform, request-access form, and jeremy@intentsolutions.io |
| Notable Clients | [[PUBLICLY_NAMED_CLIENTS]] |
| Customers Served | [[VERIFIED_PRACTITIONERS_SERVED]] |
| Projects Delivered | [[VERIFIED_PROOF_PATHS_COMPLETED]] |
| Competitors | [[NAMED_ALTERNATIVES]] |
| Social | Intent Solutions on GitHub and Jeremy Longshore on GitHub |
| Part of | Intent Solutions, with Demos, Omarchy, and Tons of Skills |
Frequently asked questions
What is Intent Solutions Learn, exactly?
It is a selective practitioner practice for people building agentic systems. It teaches the Intent method through live review, production-shaped work, and optional proof paths.
Who is it for, and who is it not for?
It is for serious builders who can show current work and want their claims tested. It is not designed as a broad tool directory, passive video library, or beginner course bundle.
How is this different from a self-paced course platform?
The practice centers on live review, peer density, and evidence from production-shaped work. Course material supports that motion instead of replacing judgment with completion badges.
How do I start?
Use the request-access form and describe the work you are doing now. The team reviews fit, then approved practitioners enter the platform and begin with the appropriate practice path.
What does access cost?
Current pricing and contract terms are [[PRICING_AND_CONTRACT_MODEL]]. The team will state the applicable terms before a practitioner commits.
How does Learn relate to Intent Solutions?
Learn is the practitioner education property in the Intent Solutions network. The parent practice builds accountable systems, while Demos, Omarchy, and Tons of Skills publish inspectable examples and reusable tooling.
Where can I inspect the evidence?
Intent Solutions Demos publishes working systems and dated evidence, while Omarchy and Tons of Skills expose source-backed tools. Learn members build their own proof through the practice and its optional paths.
Access starts with a concrete system, constraint, or proof question.