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Capture moonshots/pipe-dreams (own model, memory-as-native-vectors, prompt compression, RTO/cfr-core tooling) so they don't derail current work but aren't lost. The discipline: park what's "in the way of the point," ship the working thing, revisit when it becomes the point. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
58 lines
2.9 KiB
Markdown
58 lines
2.9 KiB
Markdown
# Parked Ideas — Lyra
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Moonshots, pipe dreams, and "doesn't exist yet" ideas. Captured here so they
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**don't derail current work** — and so they're never lost.
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**The rule:** when an idea shows up mid-snag, ask *"is this the point, or in the
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way of the point?"* If it's the point, we build it. If it's in the way, we park
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it here, use the boring existing tool for now, and come back when it's the point.
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**Honesty policy:** for each idea, note whether it doesn't exist because it's
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*hard/uneconomical* (someone tried) or because *nobody's bothered* (a real gap).
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Pick battles accordingly.
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Status: 🌙 moonshot (needs big prerequisites) · 🔬 research · 🛠️ buildable-soon
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---
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## 🌙 Build / fine-tune our own model
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Full control of persona and character, no RLHF "helpful assistant" tics baked in
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(the thing mini/qwen-14b kept fighting us on). A model that *is* Lyra rather than
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one we prompt into being her.
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- **Why parked:** needs a working system first to know what we're actually
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optimizing for; training/fine-tuning infra; data (we now *have* 18 months of
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real conversations — a genuine asset for this).
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- **Unblocks when:** the working system has taught us its real limits, and we
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have a clear target for what the model must do better than off-the-shelf.
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- **Exists?** Fine-tuning exists; a model purpose-built as a *persistent self*
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with native memory does not. Real gap, not a dead end.
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## 🔬 Memory as native vectors ("everything in numbers behind the scenes")
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Instead of re-injecting human-readable text every turn, feed memory to the model
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as learned vectors it natively consumes (soft prompts / gist tokens /
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memory-augmented transformer, à la RETRO / Memorizing Transformers).
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- **Why parked:** impossible on API models (they eat tokens, re-embed text with
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their own layer; our stored vectors are meaningless to them). Requires owning
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the model internals → depends on the "build our own model" idea above.
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- **Brain analogy:** this is closer to how *humans* store memory than text is —
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which is exactly why it's interesting for the emergence goal.
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- **Exists?** Active research, not productized. Real frontier.
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## 🛠️ Prompt compression (LLMLingua-style)
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A model that drops low-information tokens to shrink the prompt 2–5× before it
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hits the LLM. The practical, today-version of "make the context denser."
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- **Why parked (for now):** 15k-char context isn't actually hurting us yet
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(~1¢/turn on gpt-4o; MI50 prefill is fixed by prompt caching). Revisit if
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context cost becomes a real problem.
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- **Exists?** Yes, usable. Just adds a dependency + step.
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## 🛠️ Deterministic poker tooling (RTO + cfr-core)
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Wire Lyra to Brian's own GTO/solver projects so ICM, equities, and ranges come
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from real computation, never LLM guesses.
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- **Why parked:** RTO/cfr-core aren't API-ready yet. This is roadmap, not a
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pipe dream — promote it once those expose endpoints.
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---
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*Add to this freely. A parked idea isn't a rejected idea — it's a scheduled one.*
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