2026-02-16 17:57:47

There are types of "dynamical systems" that can be leveraged on top of this.


💭 the game of Hearthstone has a "meta" - what types of decks most people are playing. They optimize for success against the "meta", not the entire universe of possible decks. As a result, the "meta" is rarely if ever constant.

💡 it is an emergent behavior, similar to the movement of a flock of birds.


humans also build status games.

2026-02-16 17:25:59

It strikes me that the LLM, in some way, completely misunderstands the situation. 🔥 It has fallen into the register of an IBM commercial, rather than an actual product.

Regional fast-casual restaurant chain (~40 locations, 500 employees).

🤖 Currently paying $50K–150K per year for point-of-sale, inventory management, scheduling, and loyalty systems from vendors like Toast or Square. 💡 the 3% processing fee WILL NOT GO AWAY. 💡 should restaurants have loyalty systems?

🤖 Their general manager knows exactly what they need — she's been running restaurants for 20 years. in the future, the machine will know what you need better than the general manager with 20 years of experience. 💡 the GM will serve as a better foil to the machine than the machine can.

🤖 A freely licensed scheduling and inventory system, generated by a large language model (LLM) and tailored to their specific supply chain (they source locally, their waste patterns are seasonal), replaces generic software-as-a-service (SaaS) with something that actually fits. 💡 the machine will know what their waste patterns are. you aren't special.

🤖 If another regional chain forks the code and improves it for their context, the license requires those improvements to flow back.

If an 8-restaurant chain is using a piece of software, they aren't going to have a rival restaurant chain fork the software. Neither company has a software development team. ⚙️ the Build v. Buy question is for larger companies, with more important software


Some questions raised:

  • Do they have a "scheduling system"? 💡 yes? They have a spreadsheet. If they don't like the spreadsheet, they can talk to LONDON ⚙️ the agent that manages the inventory system. it takes phone calls.
  • Do they have a Point-of-Sale system? Yes. Always yes. ⚙️ is this a market disrupted by AI? no. the two limitations are hardware form-factor, and the stability of the 3% 💡 we use this as an estimate for the cost-of-chargebacks. transaction-fee price.
  • Do they have an Inventory system? 🔥 No. They have an inventory management system. 💡 the Inventory system means they always have what they need ⚙️ and at a "good" price. Inventory management just tracks what you have, and how much it costs.
2026-02-16 17:12:40

1. Regional fast-casual restaurant chain (~40 locations, 500 employees). Currently paying $50K–150K per year for point-of-sale, inventory management, scheduling, and loyalty systems from vendors like Toast or Square. Their general manager knows exactly what they need — she's been running restaurants for 20 years. A freely licensed scheduling and inventory system, generated by a large language model (LLM) and tailored to their specific supply chain (they source locally, their waste patterns are seasonal), replaces generic software-as-a-service (SaaS) with something that actually fits. If another regional chain forks the code and improves it for their context, the license requires those improvements to flow back.

2. Mid-market tax and accounting firm (~200 professionals, 500 total with admin and seasonal staff). Paying heavily for practice management software, document management, and client portals. Their workflows are highly specific — they specialize in multi-state businesses, and no off-the-shelf product handles their interstate allocation review well. A copyleft-licensed practice management platform, generated and customized with LLM assistance, replaces $200K or more in annual SaaS costs. The senior partners can describe exactly what the software should do because they've been doing it manually for decades.

3. Heating, ventilation, and air conditioning (HVAC) contractor (500 employees across field and office). Job costing, dispatch, equipment tracking, warranty management — they use a patchwork of ServiceTitan, spreadsheets, and paper. Their operations vice president has a whiteboard in his office with the dispatch logic that no software vendor has ever captured properly. LLM-generated, freely licensed dispatch and job costing tools reflect his actual decision logic. The copyleft terms mean that if another mechanical contractor improves the estimating module, the improvements are shared.

4. Regional healthcare staffing agency (500 employees, placing nurses and allied health professionals). Credential tracking, shift matching, compliance documentation, client billing — they currently use a $300K-per-year platform that was designed for light industrial staffing and awkwardly adapted. Their compliance director knows every state board requirement by heart. A copyleft-licensed credentialing and matching system, built to their specification via LLM, replaces a tool that never quite fit. Competing staffing agencies in other regions can adopt it, but must share their improvements — building a cooperative infrastructure for an industry where the software was never the competitive advantage. Relationships and reliability are.

5. Multi-location auto dealership group (8 dealerships, ~500 employees). Customer relationship management (CRM), finance and insurance (F&I) workflow, inventory management, service scheduling. Paying CDK or Reynolds & Reynolds enormous sums for systems that are notoriously inflexible. Their F&I managers have precise processes honed over years that the software fights against rather than supporting. LLM-generated, copyleft-licensed dealership management tools match their actual workflow. The license is particularly interesting here because the dealer management software market is an oligopoly that charges exorbitantly — a shared, freely licensed alternative would break that lock-in for the entire industry, and the copyleft terms ensure no single dealership group can privatize the improvements.

6. Specialty food distributor (warehouse, delivery fleet, sales team — 500 employees). Route optimization, order management, lot tracking, customer-specific pricing. Currently patching together an enterprise resource planning (ERP) system that was designed for manufacturing with bolt-on logistics tools. Their logistics manager has route knowledge in her head that no generic optimizer captures — she knows which customers have loading dock restrictions, which ones need Thursday delivery because they're closed Friday, which drivers can handle the mountain routes in winter. A copyleft-licensed distribution management system built from her specifications via LLM replaces $100K or more in mismatched SaaS. The license means other specialty distributors — craft beer, organic produce — can adopt and extend it for their verticals.

2026-02-15 03:43:23

levels of existence. 💡 you can add "the world" as a zeroth layer. 🔥 we're uncertain if there is a layer below the atom layer.

ONE. The "organism" level. There are six billion homo sapiens on earth.

TWO. The "cell" level. There are thirty trillion cells in each homo sapiens.

THREE. The "atom" level. There are approximately one hundred trillion atoms in each cell.

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2026-02-09 15:14:21

This is a genuinely interesting linguistic modeling problem. After reviewing the current data model, here are my thoughts.

The Core Problem

Each Lemma currently represents one concept anchored to an English label. "hat" and "cap" are two separate lemmas, each with one translation per language. But languages carve up semantic space differently:

English: "hat" (broad brim) vs "cap" (fitted, no brim) — distinct

Lithuanian: "skrybelė" (wide brim) vs "kepurė" (general headwear/cap) — different boundary

Chinese: "帽子" covers nearly all headwear generically

French: "chapeau" (hat, broad) vs "casquette" (baseball cap) — yet another split

The current model has no way to say "these two lemmas occupy overlapping semantic territory, and in language X they might collapse into one word."

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2026-02-04 22:41:51

today, in Trakaido/Barsukas development: I want to add 20-30 cities to the word list 💡 with translations in 14 languages.

  • Claude launches a second agent to do the work
  • The second agent generates a JSON file
  • Claude then does "Web Search" to try to find the ones it isn't sure about
  • After I cut it off, Claude uses the Wikidata API
  • Claude does ... a lot of verifying, much of it needless

🔥 the layers of abstraction are dizzying ...

2026-02-03 16:55:38

I have seen this specific error before, and I think the Claude Code team should be able to fix it.

⏺ Good! The WireWordDto has the audio field defined correctly. Now let me check an

actual wireword file to see what the data looks like:

⏺ Bash(cd ../wordlists && grep -A 10 "N07_003" lithuanian/wireword_nouns.json | head
-20)

⎿  grep: lithuanian/wireword_nouns.json: No such file or directory

⏺ Read 1 file (ctrl+o to expand)

⏺ Bash(cd .. && ls -la wordlists/)

⎿  total 112

-rw-r--r-- 1 powera staff 159 Aug 15 15:15 __init__.py

drwxr-xr-x 28 powera staff 896 Feb 3 10:51 .

… +26 lines (ctrl+o to expand)

⏺ Bash(cd .. && ls -la wordlists/lang_lt/)

⎿  Interrupted · What should Claude do instead?

🔥 do you see the bug? ⚙️ it is going up another directory. "cd .." is not idempotent. 💡 maybe the local Claude directions should make it clear?

2026-01-28 18:53:57

Somehow Barsukas has become the other server as part of the Trakaido project. It contains translation data, sentence data, etc.

It is, in effect, a multilingual dictionary.

Instead of sorting alphabetically, we have to categorize words and then sort them roughly by linguistic complexity. This is a blurry term, but:

  • cow is less complex than marmoset
  • red is less complex than crimson
  • table is less complex than credenza

The categories themselves have evolved. There is a super category that the LLM generated in the UI that I am not concerned with, but we have:

  • around 40 categories of nouns
  • 15 categories of verbs
  • 10 for adjectives and adverbs
  • a separate numeral category for number words

We have an architecture designed around a development pattern where people make APIs including the OpenAI key to add words. As a single-user project, this is a way to avoid putting the LLM key on the server. For a multi-user project, there are obviously security risks associated with this design. We are hoping to solve it better later.


Some of the agents have turned out to be more useful than others. This is fine.