Three layers stacked. Corpus — 2,400+ transcripts, 247,000 chunks, indexed for sub-second retrieval. Canon — every load-bearing definition locked at the system level so pretraining noise can’t override it. Calibration — every error caught becomes a spec the next day. The composition is the product.
The gems are real. They are also buried inside ninety-minute Tuesday livestreams where Mike will spend forty minutes setting up the concept, twenty minutes circling adjacent to it, and ten minutes actually delivering it.
The concept you need is in there. Somewhere between minute fourteen and minute eighty-seven. Mike circles it three times. ALGOMARK reads all three and quotes the cleanest one. 10+ years of mentorship is several-thousand hours of footage — the methodology is in there, alongside three tangents about Forbes and a riff or two on world powers. The volume is the price of admission, and ALGOMARK pays it for you.
Transcripts read end-to-end
0+
Mentorships, livestreams, private sessions, study notes.
Vector embeddings
0K+
Dense retrieval over the full corpus — not surface-level keyword search.
Hours of source footage
0+
Five years of public ICT mentorship, distilled into sub-second retrieval.
Retrieval latency
0s
HNSW vector index over the full corpus. Sub-second on every query.
The pretraining problem
Why generic AI cannot answer ICT reliably.
Claude, ChatGPT, every general-purpose LLM was trained on the open web. The web has thousands of ICT explanations written by SMC educators, TradingView authors, YouTube comment threads, and forum posts — and a huge chunk of them get core ICT concepts subtly wrong. When a generic AI is asked an ICT question, it falls back on its pretrained “consensus” — which is contaminated with non-canonical SMC paraphrasing.
The contamination is not theoretical. Pick a load-bearing concept and watch a generic model walk you into the wrong definition in confident prose. Breaker Block is the canonical example. Ask the open web — and therefore any generic AI — what a Breaker is. Then ask ICT.
Wrong — web consensus
Breaker Block · common SMC mis-definition
“A Breaker is the candle at the original swing point that flips polarity once the swing is violated.”
The web’s most common Breaker definition. Found on TradingView, in YouTube comments, on SMC blogs. Confidently restated by every generic LLM. It is wrong.
→
Right — ICT canon
Breaker Block · from the corpus
A bullish Breaker forms when price takes out a swing LOW. The Breaker is the highest up-close candle within the swing-down leg that did the violating — confirmed when price reclaims it from above as support.
ICT’s actual mechanic. Drawn directly from the corpus. A Breaker is not the swing candle. It is the up-close candle inside the leg that violated the swing — reclaimed from the opposite side. Bearish mirrors. The web definition is not a paraphrase. It is a different concept entirely.
One word changed — “swing point” vs “up-close candle inside the leg that violated the swing” — and the entire trade location moves. This is the cost of pretraining contamination. Multiply by every load-bearing ICT concept and the gap between “sounds ICT” and “is ICT” becomes a chasm.
The solution — three layers
How ALGOMARK gets it right.
One layer would not be enough. Retrieval alone leaves the model picking among contaminated paraphrases. A locked spec without retrieval is brittle. A static system rots. The three layers compose, and the composition is the product.
Every public ICT mentorship file ALGOMARK could surface — mentorships, livestreams, private sessions, study notes — ingested whole. Chunked into roughly five-hundred-token segments that preserve section path and timestamp. Embedded into more than 247,000 vector embeddings via OpenAI text-embedding-3-large.
Stored in Supabase with pgvector and HNSW indexing for sub-second similarity search across the full corpus. Source attribution stays glued to every chunk — filename, section heading, timestamp — so the answer can always point back to where it came from. The corpus grows weekly. New transcripts in, fresh embeddings out.
2,400+ transcripts•247K embeddings•1,536-dim vectors•HNSW indexed•~300ms per query
II.
The Canon
Pretraining contamination, solved by lock.
Retrieval narrows. The canon decides. Every load-bearing ICT concept — FVG taxonomy, PD Arrays, time windows, terminology — has a locked spec baked into the system prompt. When the model is asked about Breaker, it does not freelance from pretrained consensus. It reads the lock first.
The canon does more than assert what is true. It actively rejects the common mis-definitions. The system prompt names the wrong web definitions and forbids them by name — so the moment a contaminated paraphrase tries to surface, the model overrides with the canonical mechanic. Curated by the founder — an ICT student of five-plus years.
Spec excerpt — Breaker
CANON: Bullish Breaker = highest up-close candle within the swing-down leg that violated a swing low; reclaimed from above as support.
REJECT: “the candle at the original swing point that flips polarity” — web mis-definition. DO NOT USE.
The corpus is not static and the canon is not finished. Each error caught — by the founder, by a member, by a careful trader on a live session — becomes a locked spec inside twenty-four hours. The canon ratchets monotonically forward. It never regresses.
Soon, traders will flag inaccuracies directly inside chat. Submit a correction. The founder reviews. If it lands, it gets locked into canon — and the contributor gets a notification: “Your contribution improved ALGOMARK.” Crowdsourced calibration scaling beyond a single founder’s bandwidth, with quality preserved by review. The product gets sharper while you sleep.
24h spec-lock turnaround•Monotonic forward only•In-chat correction flow — shipping next
The pipeline
From Mike’s voice to your screen — the pipeline.
Six nodes between the source recording and the answer on your screen. Source attribution survives every step. No invented filenames. No invented timestamps. The pipeline does not produce sentences — it produces sourced retrievals that read like sentences.
Step 01
Transcript
2,400+ files
Step 02
Chunker
~500-token segments
Step 03
Embeddings
247K · 1,536-dim
Step 04
Retrieval
HNSW · sub-second
Step 05
Canon-locked prompt
Spec overrides ambiguity
Step 06
Sourced answer
Quote · file · timestamp
Every step preserves source attribution. No hallucination. No invented filenames. Real corpus, real timestamps. The format is the format. If a citation does not resolve to a real file at a real timestamp, it does not ship.
Why this works
The combination is the moat.
One ingredient is a feature. Two ingredients is a workflow. Three ingredients is a category. The strength of ALGOMARK is not any single layer — it is the lock between them.
Generic AI
Has the model. Lacks the corpus.
Other ICT educators
Have the corpus access. Lack the calibration loop.
ALGOMARK
Has all three. The combination is what makes it reference-grade.
The promise
ALGOMARK at launch is the most accurate ICT AI in existence.
Not because the launch is perfect — because every error caught makes it sharper, and the founder catches errors faster than any competitor’s process can match. The product is built to ratchet forward. Use it today; it will be better tomorrow.
Bring a real question
The citation arrives with the answer.
Three queries free, daily. Operator at $29/mo — 200 queries a day, full retrieval depth, conversations that persist. The corpus is the same on both tiers. Only the volume changes.