# Show HN — AccelaStudy AI

**Post day/time:** Tue May 12, 2026 — 9:00 AM ET (HN front-page sweet spot)
**Posted by:** Charles Sieg (use the actual founder account, not a throwaway)

## Title (80 chars max — HN truncates harder than that, target 60-70)

> Show HN: I built an adaptive learning platform with Claude in 81 days

**Alternates** (test in your head — pick the one you'd click):

- `Show HN: AccelaStudy AI – adaptive learning engine, built solo with Claude in 81 days`
- `Show HN: One human + Claude, 81 days, 929 certs, 29 patents`
- `Show HN: AccelaStudy – I built an adaptive cert-prep platform solo with Claude`

The first one is strongest. "Show HN: I built X with Claude in Y days" reads as a confident, transparent technical claim. It sets up the natural HN question — "wait, really?" — which the URL visit then answers.

## URL

> https://accelastudy.ai

The home page is the demo — no account required to try the engine. HN clicks see the working product immediately, not a marketing wall.

## Body Text (HN's "text" field — keep it tight, no marketing tone)

> AccelaStudy AI is an adaptive learning platform for certification + standardized-test prep. The differentiator from existing tools is real-time root-cause gap detection: when you get a question wrong, the engine traces the failure back through the knowledge graph to the actual missing prerequisite, instead of just marking the topic red.
>
> A few specifics that might interest this audience:
>
> - Per-interaction model updates run on a GPU-resident path — ~under 2 milliseconds per update, 10× lower latency than CPU-based alternatives, approximately 5× lower energy per update versus the CPU baseline (empirical, not patent-claimed). Python, PyTorch, gRPC.
> - The engine maintains a real-time mathematical model of each learner's knowledge state, updated per-answer rather than nightly-batched as most adaptive systems do.
> - 20+ interactive activity formats — selected dynamically based on which format will reveal the most about the learner's state for a given concept (information-gain optimization).
> - Pass-probability prediction uses probabilistic simulation of 2,000 exam attempts with confidence bands.
> - Cross-domain transfer: prior knowledge credits forward across related exams (e.g., AWS CCP → AWS SAA gets ~60% credit on overlap).
> - Content corpus is AI-synthesized via a a multi-stage validation pipeline. Approximately at a small fraction of human-authoring cost. 1,400,000+ questions across 929 exam specifications today.
>
> Practical scope at launch: 929 certification + standardized-test specifications (AWS, Azure, GCP, CompTIA, CISSP, AP, MCAT, LSAT, SAT, etc.), 2196 hands-on console-simulator labs (real DOM, real assertions), 20574 adaptive lessons.
>
> The reason I'm posting this on HN is because I want technical scrutiny on three claims that matter most to this audience:
>
> 1. The "<2 ms model update" claim — happy to walk through the implementation.
> 2. The information-gain activity-selection claim — would value pushback on the math.
> 3. The "compute-efficient synthesis pipeline with a multi-stage validation pipeline" claim — there's a paper-shaped writeup coming, but I'll answer questions today.
>
> I'm a solo founder. I built the entire platform — engine, 4 backend services, 10 native clients, 25 internal tools, 19 marketing sites, the 637-claim patent portfolio — in 81 days, working with Anthropic's Claude as my only engineering collaborator. 74 repos, 1.27 million lines of code, 24,800+ tests, 8,200+ commits. No employees, no contractors, no funding.
>
> Native iOS / iPad / Mac / Windows / Linux apps follow on Monday, June 1 (App Store review timing). The web is what I'm asking you to look at today.
>
> Try the free demo at https://accelastudy.ai (no account required — the home page is the demo).
>
> Happy to answer anything technical or business.

## First Comment (post yourself within 5 minutes of submission, sets the engagement tone)

> Author here. A few notes on what I'd most value feedback on:
>
> **The hardest content call:** I rejected human-authored content entirely. Every question and every lesson is synthesized. This means I can launch a new exam in hours instead of months, but it also means I'm betting that a multi-stage validation pipeline keeps quality above the bar where students would notice. Acceptance rates run 90-96% per pass. If anyone here has done large-scale synthesis-then-validate work, I'd love to compare notes.
>
> **The performance call:** Most adaptive systems update their learner models in nightly batches because the per-update path round-trips through CPU. We update per-answer (~under 2 milliseconds) on a GPU-resident path, which lets the engine respond to confidence signals in the same session rather than the next day. Specifics of the architecture are in the patent spec; happy to discuss tradeoffs of per-answer vs batched at the system level.
>
> **The Claude collaboration model:** I'm not the kind of "I used AI to write code" founder who wrote a wrapper around the OpenAI API and called it a startup. The platform is structurally large — 74 repositories, 1.27 million lines, real ML, real infra, real integration tests — and I made every architectural decision. Claude implemented under direction with code review on every change. The actual unit of production is the human-AI pair; calling it "solo founder" undersells Claude and calling it "AI did it" undersells the architectural work. I'll answer specific questions about what that workflow actually looks like.

## Engagement strategy (Tue May 12 all day)

- **Reply within 30 minutes** to every top-level comment. HN front page survival depends on visible engagement velocity in the first 4 hours.
- **Be technical first, marketing second.** If someone asks "how does the knowledge-model work," answer with the actual approach, not a product pitch.
- **Be honest about limitations.** "We don't yet do X because Y" beats "everything is great."
- **Acknowledge prior art.** If someone mentions Anki, Knewton, ALEKS, etc., engage substantively — don't dismiss them. The HN audience knows the field.
- **Don't argue with downvotes.** If a take goes negative, address the substance, not the score.

## Title-line A/B test (do this in your head only — pick one and ship)

| Title | Why it might win |
|---|---|
| `Show HN: I built an adaptive learning platform with Claude in 81 days` | Front-loads the unique angle; "with Claude" is what HN is curious about right now |
| `Show HN: AccelaStudy – adaptive cert prep with sub-2ms knowledge updates` | Front-loads the technical claim; appeals to the engineers in the audience |
| `Show HN: AccelaStudy – 929 certs, over 1.4 million AI-synthesized questions` | Front-loads scale; might read as too marketing-y |

**Recommendation: title #1.** The "with Claude in 81 days" framing is the highest-leverage hook this audience will see today.
