Really? That’s… astonishing.
You know what? I hope they do that. So long as they give fair warning, so I don’t rock up to work one day and no longer have access to work files / OAuth / 2FA without warning.
They send an email, I can escalate. Not my problem then.
Can’t says I’ve noticed, because apart from the stupid account, I don’t use google.
Don’t. Chances of finding a pre 2021/23 model are slim, and if you happen to connect it to the net carelessly, it will auto bork itself.
Get a raspberry pi
If you mean the GCCWTV, google patched that out in 2023. You can still ADB into them just fine to ADB push or pull apps or files (after you install adbtools), but you cannot install CFW (like lineageOS) any more.
I think probably the best way to get something like lineageOS on your TV is to get a raspberry pi (or a CM4 module + HDMI board) and flash that. You should be able to pair your preexisting Chromecast remote with it and the interface should be seemless.
^ I can confirm this. It’s why I refuse to interact on Reddit anymore. Fuck em.
I’m building one. It treats you how you treat it, by classifying tone and content and responding in kind, adapting on the fly / with decay curves. I do it using a local classifier swarm (7 micro, sub LLMs) and a decision tree.
You can set it to IDGAF mode by default. Still useful…just zero fucks given.
(Ignore the “Powered by OpenAi” bit. That because GPTmobile thinks anything using OpenAI shaped endpoint is an OAi model)
https://bobbyllm.github.io/llama-conductor/blog/claude-in-a-can-1/
FWIW Extra shit I cooked last night. It’s live now, so deserves a PS: of its own
PPS: I inbuilt as spam blocker as well.
Enjoy :) Blurb below
“But what if it just… Googled it?”
We can do that. But better.
You: Who won best picture at the 97th Academy Awards?
Model: Anora won best picture at the 97th Academy Awards.
See: https://www.wdsu.com/article/2025-oscars-biggest-moments/64003102
Confidence: medium | Source: Web
Without >web, that same 4B model said “The Fabelmans.” Then when I pushed it, “Cannes Film Festival.” With web retrieval, the router searches the internet, scores every result deterministically (phrase match + token overlap + domain trust), and only accepts evidence that passes a hard threshold. Garbage results get rejected, not served. The model never touches the answer - it’s extracted straight from the evidence.
Retrieval cascade: Cheatsheets → Wiki → Web → Model. Each step fires only if the previous one missed. The model is last resort, not first call. Sound familiar?
I asked it who wrote a paper and it invented “Dr. David J. G. Smith” - a person who does not exist. After wiring DOI/Crossref fallback for academic metadata:
You: Who wrote The Anatomy of a Large-Scale Hypertextual Web Search Engine?
Model: Sergey Brin and Lawrence Page.
See: https://research.google/pubs/the-anatomy-of-a-large-scale-hypertextual-web-search-engine/
Confidence: medium | Source: Web
Deterministic extraction from metadata. No model synthesis.
>web is provider-agnostic - ships with DuckDuckGo (no API key, no account) and supports Tavily, SearxNG, or your own adapter. Add your own trusted domains in one config line (there are a bunch baked in already, like pubmed). Every answer comes with a See: URL so you can verify with one click. Receipts, not pinky promises. PS: I even cooked in allow-list / deny-list domain filters, junk-domain blocklist and ad/tracker URL rejection so your results don’t get fouled with low quality spam shit.
“…specifically crafted to demonstrate tasks that humans complete easily”
Motherfucker, I can’t work out Minesweeper. I got zero fucking chance with your mystery box bloop game.
You’re describing trust dynamics right and that’s exactly why this project doesn’t ask you to trust the model. It asks you to trust observable outputs: provenance labels, deterministic lanes, fail-loud behaviour.
When it fails, you can see exactly which layer failed and why. Then you can fix it yourself. That’s more than you get right now (and in part why LLMs are considered toxic).
The correction mechanism is explicit rather than hoped for (“it learns” or “it earns my trust back”): you encode the fix via cheatsheets, memory, or lane contracts and it sticks permanently.
The model can’t drift back to the wrong answer. That’s not the model earning trust back - it’s you patching the ground truth it reasons from. Progress is measured in artifacts, not vibes.
Until someone makes better AI, that’s all we’ve got. Generally, we don’t get even this much.
Sadly, AI isn’t “one mind learning”; it can’t. So trust is earned by shrinking failure classes and proving it stuck again and again and again (aka making sure the tool does what it should be doing).
Whether that’s satisfying in the way a person earning trust back is satisfying - look, honestly, probably not. But it’s more auditable.
LLMs aren’t people and I’m ok with meeting them where they are.
Nope.
Source: Model is not pretending otherwise
It is basically “priors lane.” That’s the point of the label: explicit uncertainty, not fake certainty.
Source footer is harness-generated, not model-authored
In this stack, footer normalization happens post-generation in Python. I’ve specifically hardened this because of earlier bleed cases. So the model does not get to self-award Wiki/Docs/Cheatsheets etc.
Model lane is controlled, not roulette
So yes: Source: Model means “less trustworthy, verify me.” Always do that. Don’t trust the stochastic parrot.
But also no: it’s not equivalent to a silent hallucination system pretending to be grounded. That’s exactly what the provenance layer is there to prevent.
Sure.
Source means where the answer was grounded, not whether an LLM wrote the sentence.
Quick split:
Source: Model
No reliable grounding lane fired. It’s model priors.
Source: Context (Contextual)
A deterministic lane fired and built a structured context for the turn (for example state/math carry-forward, bounded prior-turn facts, or a forced context frame), and the answer is expected to come from that frame.
Key clarification:
Context only when it is captured into a bounded deterministic frame/lane and used as grounding.Model.Why this is more deterministic:
Concrete example:
A Jar has 12 marbles. I remove 3. How many left?9, injects structured context.Source: Context.If that lane doesn’t fire (or parse fails), it falls back to normal generation and you get Source: Model.
So Context is not “perfect truth”; it means “grounded via deterministic context pipeline, not free priors.”
I hope that clarifies. I can try a different way if not; my brain is inside the code so much sometimes I forget what’s obvious to me really isn’t obvious.
^ that. Thank you.
It’s for everyone to use :)
I get that it’s maybe an acquired taste though.
Steal what you can, make it better, and then I can steal it back.
And thanks for the star!
I genuinely don’t know. A small part of llama-conductor is a triple pass RAG system, using Qdrant, but the interesting bit is what sits on top of it. It’s a thinker/critic/thinker pipeline over RAG retrieval.
I built it that way based what the research shows works best to reduce hallucinations
Inverse Knowledge Search over Verifiable Reasoning
To be honest, I have been looking at converting to CAG (Cache Augmented Generation) or GAG (Graph Augmented Generation). The issues are - GAG still has hops, and CAG eats VRAM fast. Technically, for a small, curated domain, CAG potentially outperforms RAG (because you eliminate the retrieval lottery entirely). But on a potato that VRAM ceiling arrives fast.
OTOH, for a domain-specific knowledge base like you’re describing, CAG is worth serious evaluation.
Needs more braining on my end.
You’re welcome. Hope it makes sense. If not, you can marvel at the (many, many) nestled swears in my -commit messages.
Can’t it source other LLM outputs as “verified source” and thus still say whatever sounds good, like any LLM?
No. The footer tells you what the source is. Anything the model generates on its own is confidence: unverified | source: model - explicitly flagged by default. To get to source: docs or source: scratchpad, it needs direct, traceable, human-originated provenance. You control what goes in. The FAQ outlines the sources and strength rankings; it’s not vibes.
Providing “technical” verification, e.g. SHA, gives no insurance about the content itself being from a reputable source.
SHA verifies the document hasn’t been altered since it entered your stack. Source quality is your call. GIGO is always an issue, but if you scope the source correctly it won’t drift. And if it does, you’ll know, because the footer tells you exactly where the answer came from.
The cheatsheet system is the clearest example of how this works in practice: you define terms once in a JSONL file, the model pegs its reasoning to your definition forever. It can’t revert to something you didn’t teach it. That fingerprint is over everything.
… the user STILL has to verify that whatever is provided is coherent and a third party is actually a good source.
Yes, deliberately. That’s a feature.
Like I said, most LLM tools are trying to replace your thinking, this one isn’t. The human stays in the loop. The model’s limitations are visible. You decide what to trust. Maybe that’s enough, maybe it isn’t.
EDIT: giant wall of text. See - https://codeberg.org/BobbyLLM/llama-conductor#some-problems-this-solves
“I have introduced myself. You have introduced yourself. This was a very good conversation.”
Confidence: Zero | Source: Model
Well, you know what they say - there’s no force quite like brute force :)
But to reply in specific:
[1] Decision tree + regex: correct, and intentional. The transparency is a feature not a bug. You can read the routing logic, audit it, and know exactly why a given turn went where it did. A fine-tuned routing model reintroduces the black box problem at the routing layer itself - and if it misclassifies, what catches it? You’ve pushed the problem one layer up, not solved it.
[2] Deterministic-first doesn’t mean deterministic-only. Open-ended turns go to the model by design - I’m not trying to regex all language, just not use an LLM where a calculator or a SHA check works better. The model is still involved. Case in point - see the car wash test.
[3] On edge cases - yep, and that’s what 8,764 benchmark runs were for. Failures are taxonomized and patchable at the routing layer without touching the model. If a rule fails, I can show the exact failure and patch it. Yeah, that’s going to be wack-a-mole for a while but…if a routing model fails, I’d need new training data and still may not know why. Models are inherently black box. Python code (as your robots have shown you) is the opposite.
My way, I know where the fuck up is and I can figure out a global maxima solution myself, cheap and easy.
[4] On the fine-tune suggestion: on a 4GB potato, rule updates are free and immediate. Retraining cycles are…not. Send money, will will buy Strix or cloud GPU access :)
[5] The hybrid direction is already on the roadmap! TLDR: Swarm handles ambiguous routing; deterministic lanes stay for bounded and high-stakes tasks. Hybrid control + learned judgment, with measurable gates before each promotion. That sequencing is deliberate.
Slightly longer version of what that should look like:
User turn
→ Classifier (labels intent)
→ Contradiction detector (user turn + last N turns)
→ Refusal/risk assessor (user turn + classifier label)
→ State tracker (full session summary from memory)
→ Synthesiser (user turn + all worker outputs as FACTS block)
→ Critic (hunts violations in synthesiser output)
→ Output or retry
Each worker does one job with full attention on that task. The synthesiser gets verified ground truth, not raw conversation history.
I’m looking at sub-1B classifiers (SmolLM2-135M/360M, Gemma-3-270M maybe) for fast-gate roles. Phi-4-mini is already a part of the main show (used in as the second pass critic model). Unlike everyone else, I am optimizing for we in the GPU peasant class.
That’s the fine-tune direction, anyway. Scoped, staged and only where the deterministic layer can’t do the job cleanly.
PS: Say hello to the klankers and tell them to stop cloning my shit for their training. :)
Oh for sure I could…but should I have to?
The whole thing is bass-ackwards.
I reckon I’ll just leave it and see what happens. If it shits itself - awesome. Business case then and there for ditching big Evil.
We don’t need this shit, no one asked us our opinion on using it, it just … “became a requirement to have a Google account” one day.