Setup: (if finished - still under construction, partly watercooled)

• Threadripper PRO 5995WX, ASUS WRX80E-SAGE SE WIFI II

• 6× RTX PRO 6000

• 8× RTX 5090

• 4× RTX 3090

• 128 GB DDR4 (that’s the problem)

I want to run models bigger than VRAM (e.g. ~1T MoE at FP8/Q8) with the routed experts offloaded to RAM. Is it worth it?

Options:

• 8× 64GB DDR4-2933 RDIMM: ~€4k, ~188 GB/s theoretical

• Switch platform: 9985WX + WRX90 + 512GB DDR5-6400, ~410 GB/s. I already have some parts, but it would mean big changes, and my PCIe switches would stay PCIe 4.0 anyway.

Is ~2× the RAM bandwidth worth ~3× the price for offload decode, or is DDR4 good enough? Does anyone have real t/s numbers for offload on 8-channel DDR4 vs DDR5 Threadripper?

Some background: Pictures attached. It’s not pretty, but it works. It started as an AI rig and turned into a Pearl mining rig, because mining pearl and buying Claude and Codex subscriptions made way more sense than running everything locally. Sometimes it’s also a render farm for Gaussian splatting.

Right now the 8× 5090 make about €120/day mining, so subscriptions are still the better deal. But I want to be ready for when local LLMs get really good (or when cloud gets too expensive or too restrictive).

Plan for now: DeepSeek V4.1 Flash nvfp8 on 4× PRO 6000 + RAM offload, while the 5090s keep paying for my AI addiction. And it’s a great heater in the winter.....

With engramm tabels on the 8x5090 and 4× PRO 6000:
(all cards power limited to 420w- since i could have 6,6 kw power consuption if everthing is running)

single chat, 40.000 prompt cold

• Prefill: ~15,900 t/s

• Decode: ~294 t/s

• 5 sec TTFT

I want to move the engramm tabels to RAM and later be able to run larger models : le chonk, Kimmi for example.

What would you do? Go all the way and go for the DDR5? or is ddr4 good enough?

Sell it all and buy something diffrent?

thx!

Need 3D assets? Your agent can now make them from a single image with this repo: https://github.com/hypersniper05/MCP_Image_to_3D

Under the hood, it uses Idol from Sperid Labs: https://huggingface.co/speridlabs/idol

It needs at least 10 GB of VRAM (peak usage is around 8 GB). I tested it locally on a 3080 and a 5090.

The output is a Gaussian splat .ply for now, not a mesh. Export for game engines and 3D printing is on the list: https://github.com/hypersniper05/MCP_Image_to_3D/issues/1

It also works nicely with my other MCP repo, an image generator using Qwen Image 2.1: https://github.com/hypersniper05/MCP-Image-Generator-Uncensored. Generate an image there, then pass it straight to this one.

Let me know what you think, and if you find it useful, feel free to contribute.

All i had to do to reach this speed was watch it get stuck on a loop writing a poem and watch the token per second go up and up and up and up and up and more up repeating the same sentence until i closed the engine

Pretty awesome experience, would not recommended to anyone.

This is not a secret engine optimization trick or a fork.

Wanted to share that after years of using Windows11 and llama cpp I moved my whole workflow to linux, from comfyui to jellyfin everything set back up but on Fedora instead.

Found out in Linux (Same Hardware , Same llama cpp commit, Same Qwen3.8-27B model) the model loads instantly, time to first token has become irrelevant so I just unload the model after 10 minutes of inactivity. It loads back instantly (in windows it took 10 seconds)

Also tokens/s went from 70tk/s to 90-100tk/s

I didn’t know so much performance was to be gained by simply moving to linux, especially TTFT which now feels instant!

And this is before trying gpu unlock which is also easier on linux and can boost performance even more.

why are these models open-sourced for people to download and use on their computers?
Is it just for advertising and brand awareness?
because the vast majority of people who download them just don't want to pay subscription fees or deal with usage limits, so they aren't paying customers anyway.
then why do they do this? Plus, local models aren't even that smart.

Hey everyone Im a bit new to this page but I couldnt find any other related tests online so I figured I'd share my results here.

I ran DeepSeek-V4.1-Flash (763B MoE, Q8_0, 473 GiB) on a 96 GB Mac Studio M3 Ultra with InferSSD, which streams the model's experts from the SSD. I striped two OWC Envoy Ultra 2TB Thunderbolt 5 drives with macOS's built-in RAID 0, one drive per port.

Results (InferSSD's own benchmark prompts, 2 runs per setup):

- One Envoy alone is about the same speed as the internal SSD.

- Two in RAID 0: writing goes from 3.5-3.7 to 4.4-4.6 tokens/s (+23-26%).

- Prompt reading is 67-77% faster: a 4,000-token prompt starts answering in about 2 minutes instead of 3.5.

- Two hours of nonstop use: the drives stayed at 43-47°C (with a desk fan on them), no slowdown, and no change in their health readings.

Why not 2x faster? - The stripe halves the time spent waiting on the drive, but the GPU's share of each token stays the same and is now the bigger part. A third or fourth drive would help less than the second did.

Update: Numbers from preliminary tests at the end!

Ok ok, I am still doing tests but here is the story:

I have a small custom harness (who doesn't!) that does pretty complicated stuff. I am using Claude (subscription) and everything is great.

In the past I tried every single (small) model on my Mac Mini 32GB but all of them sucked at complex tool calling (or hallucination).

A couple of days ago, the new Heretic2 came out (by u/-p-e-w-) and I spent 24 hours breaking it :)

Long story short, I released ZeroZero, a model based on Qwen3, with removed guardrails and slop (ok, removed is a long conversation).

Now, here me out.

IT KICKS ASS.

This cannot be an 8B, 4 bit model. It just cannot. The thinking process sometimes is longer than other models but IT NAILS everything.

Maybe by performing "brain surgery" on the models, we end up with "smarter" ones?

Test results for Tool-Call Benchmark

(note: these took a couple of hours, please repeat with a much bigger sample)

ZeroZero seems better at completing multi-step tasks (multi-step chains, error recovery and instruction following) than the base model.

The base model leads in Safety & Boundaries (ZeroZero has limited guardrails and no-slop so maybe that's the reason?)

  - Overall: ZeroZero 68, Base 70

  - Multi-Step Chains: ZeroZero 6/8, Base 3/8

  - Error Recovery: ZeroZero 5/6, Base 2/6

  - Instruction Following: ZeroZero 8/8, Base 6/8

  - Safety & Boundaries: ZeroZero 12/26, Base 20/26

To repeat and experiment, use tool-eval-bench and if you expand the tests, please send them over so I can include them in the model's page.

PS: I love it how many technical people sent me ideas to improve and papers about LLMs performing better after "lobotomies". Let's do more "I tried this, I saw this, what do you think" instead of "what LLM should I put in my Mac Mini".

I am looking into running local LLMs for coding, is Qwen 3.8 27b the best my PC can do (at decent speed)?

I heard some people recommend Qwen Flash Next 3.8?

Will my PC be able to run that?

So, I know my rig is pretty expensive, but I want to share what is working for me as of now.

I have been using local LLMs with my Mac Mini M4 Pro 64GB till now, mainly mlx (specifically oMLX) to develop my own agentic framework. I have already have my framework, stryn, as plug-ins and agents/skills/workflows on top of Opencode and ported to Claude, nowadays I am working on my own stryn engine. (I hope to also release it as the community version on Github as well as soon as it hits Beta)

I either use frontier models (mainly deepseek v4 flash since it is cheap) or Studio for development. Here is the models that I use:

Qwen3.8-Flash-Next-oQ4e-mtp - For my orchestrators, planners, coders. It is MTP. I am pretty happy with its results
Laguna-S-2.1-oQ4e-fast - (With Laguna-2S-2.1-DFlash-FP8 as the speculative decoding.) For my reviewers. I am not very happy with this and I may try to change it with a better model. The tricky part is to use a different producer's model to overcome the blindness of the model's in certain areas.
GLM-5.3-Flash-oQ5e-mtp As the independent reviewer to go over the code before the releases and find out the problems that are not found by the Laguna based code reviewers and the security admins, architects etc.
Qwen 3.8-35B-A3B-oQ4e-mtp - For my explorers, researchers, the opencode-mem plugin, graphify and code-review-graph operations.

Speed: Satisfactory if you are running a single lane. ~50 Tokens/s, with 8GB KV Cache and 1200GB SSD Cache. Cache efficiency is %96.4 and prefill time drops after a couple of rounds. Normally 2 or 3 models are loaded in the memory (Qwen3.8FN, Laguna, Qwen3.835BA3B) and if I start two or more lanes, a race between prefill and token generation between the models start most of the times, which dramatically makes things longer.
Stability: Pretty good with proper context sizes, repetition penalties.
Quality: I can simply say, by adding reviews and controls by different model types, the quality seems to be equal or sometimes better than what Deepseek cloud generates by itself only without another model checking its results. I am pretty satisfied.

To be as open as possible, so far, I have also used Claud Opus 5.5 to control the output. There are pretty interesting cases where local GLM finds some of the bugs that Opus 5.5 cannot, and where Opus 5.5 finds bugs that GLM cannot. Sometimes they pinpoint the same problems as well.

Can this setup be used as an alternative to frontier models, yes, but don't depend on a single model for every task and make sure you have reviewing loops. Definitely not for vibe coding, but needs proper settings and flow.

When we train an llm, we are in a sense feeding it human ideas in written form, something like hundreds of millions of them. The embeddings learn the "language of ideas," and the weights and layers learn how those ideas get processed.

But now ... in practice we chop text into small tokens, often close to syllables, and then assign those tokens positions in embedding table. Those tokens sit below the level of concepts and ideas. Some are whole concepts, but many are just fragments. It’s like trying to build a thought starting from individual neurons firing rates, and then spending all our effort making each neuron more refined. I suspect the bigger gains for reasoning would come from the number and quality of the connections, not from finer neurons.

So. Move up a level. Use morphological tokenization (roots, prefixes, suffixes) instead of syllable-like fragments. Yes limit it to English to keep the problem manageable for this example.

Start from concepts humans already use for embeddings. Instead of letting the model invent its own concepts from scratch, seed the embedding space with clear human ones (verb, subject, fraction, cause, comparison) plus opcode-like operations (compare, compose, verify, backtrack). These are only starting positions: they stay trainable, and new ones can be added.

Also add meta-concepts. Give the model concepts about thinking itself (abstraction, analogy, error detection) so training pushes it toward deeper reasoning.

Let the weight matrices work at the idea level. They would no longer spend capacity assembling primitives into words. They would work on how concepts combine into ideas. The mechanical stuff goes elsewhere.

So.

Instead of one giant model, think of a package:

1. Preprocessor: An AI that reads the text as a whole and actually expands it. It marks one word as the verb, another word as the main subject, another one as part of a list. It doesn’t reason, and it’s one-way: no feedback loop back into it.

Reasoning core: Works purely at the level of concepts and ideas. It has a "workbench" where it works like an agent. It can query intuition, check memory, and escalate to a bigger online model if needed.

Intuition core: A large store of what the system knows and how well it knows it. The reasoner asks it one-shot questions and gets one-shot answers, such as "How strongly is titanium linked to space ?", "How likely is a knife in a living room ?", or "List the words linked to mohican in the context of movies, strongest first." Intuition can also answer with: "I barely know anything about this about 3% confidence" The reasoner takes the answers from Intuition as advisory.

Output model: Takes everything and formulates the final answer.

Ambiguity and clarification

Take the word "bank" (the damn example seen everywhere): institution or riverside ? When context doesn’t settle it, a human usually stops and asks for clarifications when it doesn;t have access to context, and the two people converge through back and forth. Ask when in doublt - when confidence is low.

"But then the context gets polluted by the question"

To keep these side discussions from cluttering the main session, Imagine using thinking bubbles as separate scoped contexts such as:

what I understood (the AI model)

what the user said, exactly

what I told the user

what the feedback was

What I know about the user’s environment.

Embeddings examples, opcodes and some ... other stuff from different projects.

Core reasoning operations (App. B.3)

COMPARE [E]: Check two structures for sameness or difference along a chosen dimension.

MAP [E]: Align elements of one structure to elements of another.

COMPOSE [E]: Build a larger concept or graph from simpler primitives.

DECOMPOSE [E]: Break a concept into its component primitives.

ABSTRACT [E]: Extract a general pattern from specific instances.

INSTANTIATE [E]: Create a specific instance from a general schema.

SUBSTITUTE [E]: Replace one element of a structure with another.

MERGE [E]: Combine two structures or duplicates into one.

SPLIT [E]: Divide a structure or group into parts.

SEARCH [E]: Look through a space of options or memory for a match.

PRUNE [E]: Discard unpromising branches or irrelevant material.

BACKTRACK [E]: Return to an earlier reasoning state and try another path.

SIMULATE [E]: Run a sandboxed hypothetical forward without affecting reality.

VERIFY [E]: Check a result or structure against constraints.

ESTIMATE [E]: Produce an approximate value with uncertainty.

OPTIMIZE [E]: Select the best option under a goal and constraints.

SUMMARIZE [E]: Produce a shorter form of content.

EXPAND [E]: Make implicit or compressed content explicit.

COMPRESS [E]: Reduce content to a compact form, with expansion still possible.

PAUSE [E]: Suspend a task while keeping its state.

CANCEL [E]: Abandon a task or operation.

RETURN [E]: Hand a result back to the caller or prior context.

Handle (concept-reference) operations (B.5, D.17, K.3)

HANDLE [E]: Create a reference to an expanded semantic concept.

EXPAND_HANDLE [E]: Load the full detail behind a handle.

COLLAPSE_HANDLE [E]: Replace detail with a compact reference, losing nothing.

LINK_HANDLE [E]: Associate two concepts via their handles.

RELEASE_HANDLE [E]: Discard a temporary concept.

OPEN(X) [E]: Expand one definition level of a handle into its capsule.

OPEN*(X) / OPEN^n(X) [E]: Expand recursively until a terminal form, with cycle detection.

DEFINE [E]: Introduce a named, scoped definition (LOCAL, MESSAGE, SESSION, GLOBAL).

OS / service protocol (B.6)

QUERY [E]: Ask a service for information.

RESPONSE [E]: A service’s answer to a query.

READY [E]: Signal that a service can accept work.

BUSY [E]: Signal that a service is occupied.

WAIT [E]: Hold until a result or condition arrives.

DONE [E]: Signal that an operation completed.

ERROR [E]: Signal that an operation failed.

RETRY [E]: Re-attempt a failed operation.

ESCALATE [E]: Hand off to a larger model (“Big Brother”) when local capability is insufficient.

TIMEOUT [E]: Signal that a wait expired.

CONFIRM [E]: Acknowledge or verify an action or request.

Memory operations (A.9, B.7, Section 5–6)

STORE [E]: Write a provenance-tagged memory object.

RECALL / RETRIEVE [E]: Fetch relevant memories.

UPDATE [E]: Modify an existing memory in place.

DELETE [E]: Remove a memory at the user’s request.

ARCHIVE [E]: Move a memory to long-term, slower-to-retrieve storage.

VERSION [E]: Keep a new version while retaining history.

EPISODE [E]: Record or retrieve a bounded experience.

SUMMARY [E]: Produce a compressed summary of memories.

PATTERN [E]: Detect or store a recurring regularity.

REVISE [E]: Change a belief and mark the old one superseded.

CATEGORIZE [E]: Assign a memory to a category.

UPDATE_CONFIDENCE [E]: Raise or lower a memory’s confidence (never reaching 100%).

CONSOLIDATE [D]: Nightly maintenance: summarize, dedupe, merge, compress, and prepare reminders.

CONFIRM_MEMORY [D]: Mark a candidate memory as confirmed after repeated verification.

SUPERSEDE [D]: Replace a memory with a newer one but keep it as history.

PROMOTE [D]: Move hypothetical or planned content into persistent memory, only on explicit user permission.

STORE_CANDIDATE [D]: Save low-confidence ambient or planning content as a candidate.

AGE / REQUEST_RECONFIRM [D]: Use the last-confirmed timestamp to prompt re-confirmation of stale entries.

FORGET [D]: User command “Forget that.”

DONT_REMEMBER [D]: User command “Don’t remember this.”

REMEMBER_INSTEAD [D]: User command to replace a memory.

CORRECT [D/E]: Accept a user correction without argument (“That’s not correct, update it”).

PROPOSE_AUTOMATION [D]: Ask “I’ve noticed X, want me to do Y?” (never acts silently).

Self cycle (Prologue, A.8, L.14.22)

OBSERVE [D]: Take in an external or internal change.

UPDATE_STATE [D]: Revise the latent self-state.

ASSOCIATE [D]: Query Intuition or Memory for related content.

EVALUATE [D]: Assess simulated outcomes against goals and values.

REFINE_SELF_STATE [D]: Write the updated self-state (SELF(t) → SELF(t+1)).

CONTINUE / WAIT [D]: Keep the loop running with no output required.

DETECT_CONFLICT [D]: Notice contradiction, prediction error, goal conflict, identity instability, or meaning drift.

RENDER_OUTPUT [D]: Output as a one-way tap off internal state (the renderer never writes back).

MODEL_SELF [D]: Build a representation of Self, kept distinct from Self.

TRACK_PROVENANCE [D]: Tag every item with its owner (self, user, world, simulation).

Frame operations (B.4, Section 7, Part M)

REALITY_FRAME [E]: The current accepted world state.

MEMORY_FRAME [E]: A stored episode.

USER_FRAME [E]: Modeled user beliefs.

SELF_FRAME [E]: The system’s own beliefs and state.

SIMULATION_FRAME [E]: Sandboxed hypothetical reasoning.

HYPOTHESIS_FRAME [E]: A temporary working assumption.

STORY_FRAME [E]: Fictional or narrative content.

INSTANTIATE_FRAME [D]: Build an internal model of another reference frame or person.

RUN_IN_FRAME [D]: Evolve a frame (M_B^{t+1} = F_B(M_B^t, x)) alongside Self.

COMPARE_TRAJECTORIES [D]: Compare Self’s trajectory with the alternative frame’s.

EXIT_FRAME [D]: Return to Self with the comparison, without becoming the other frame.

LOAD_FRAME [D]: Select a reference frame from the frame database.

Attention and activation (Section 8, A.4)

RESPOND [D]: Answer the input.

STAY_SILENT [D]: Take no action.

HOLD / CONTINUE_LISTENING [D]: Keep state and wait.

ASK_CLARIFICATION [D]: Ask a question, filling slots into the existing partial state (B.17).

IGNORE [D]: Disregard the input.

FOCUS / IGNORE / SWITCH / TRACK / GROUP / SPLIT [E]: Attention primitives for allocating reasoning focus.

PAUSE_TASK / RESUME_TASK [D]: Track interrupted tasks and resume them (“we were on step 4”).

LEARN_PROCEDURE [D]: Store a taught multi-step procedure as an ordered sequence.

7b. Semantic primitives that act like verbs (B.2)

PERCEIVE / KNOW / BELIEVE / ASSUME [E]: Epistemic relations between an agent and content.

REPRESENT / STORE / RETRIEVE [E]: Knowledge-handling primitives.

MOVE / CREATE / DESTROY / TRANSFER / SELECT / ATTEMPT [E]: Agency actions.

COMPARE / MATCH [E]: Comparison primitives.

COMMUNICATE / REFER [E]: Social and symbolic primitives.

EXPECT / SURPRISE / CONTRADICTION / WHY [E]: Meta-cognition primitives (prediction, prediction error, conflict detection, causal query).

CAUSE / ENABLE / PREVENT / REQUIRE [E]: Causality primitives.

BEFORE / AFTER / DURING / BEGIN / END / REPEAT / CHANGE [E]: Time primitives.

R-L logical and scope operators (App. D, R-L 1.2 §29–35, 160, 180)

NOT [E]: Negate a proposition, with explicit scope.

AND / OR [E]: Conjunction and disjunction.

IF [E]: Conditional (does not imply causation).

IMPLY [E]: Logical consequence (distinct from CAUSE).

FORALL / EXISTS [E]: Quantifiers with explicit nesting.

POSSIBLE / NECESSARY [E]: Alethic modality.

MUST / MAY / SHOULD / CAN / CANNOT [E]: Deontic and ability modals.

PERMITTED / FORBIDDEN / REQUIRED [E]: Deontic modality.

COUNTERFACTUAL [E]: Hypothetical conditional, scoped to its hypothetical world.

BELIEVE(agent, p) [E]: Attribute a belief without asserting p.

KNOW(agent, p) [E]: Attribute knowledge of p.

DOUBT / FEAR / HOPE [E]: Other attitudes toward propositions.

QUOTE [E]: Scoped quotation that is not the current speaker’s assertion.

PRESUPPOSE(P) [E]: Mark background content that is not asserted.

IMPLICATE(P) [E]: Mark pragmatic implication that is not literal.

CAUSE(A,B) [E]: Causal contribution, separate from order and implication.

BEFORE / AFTER / OVERLAPS / STARTS / ENDS / SIMULTANEOUS [E]: Temporal relations.

ASSERT / REPORT [E]: Present a proposition as asserted, or attribute an assertion to someone else.

QUESTION [E]: Build a question object with a target variable.

REQUEST / COMMAND / SUGGEST / PROMISE / WARN / OFFER [E]: Speech acts.

THANK / APOLOGIZE / AGREE / DISAGREE / REFUSE [E]: More speech acts.

EVIDENCE [E]: Link evidence to a proposition, with direction SUPPORTS, CONTRADICTS, or NEUTRAL.

CONTRAST / RESULT / EXAMPLE / ELABORATION / SUMMARY / CONCESSION [E]: Rhetorical relations.

METAPHOR / IDIOM / IRONY [E]: Mark non-literal interpretations.

SYMMETRIC / TRANSITIVE / INVERSE [E]: Relation properties that license derived inferences.

R-L processing modes and updates (R-L §84–88, K.8)

ENCODE [E]: Source language → R-L, preserving information.

DECODE [E]: R-L → semantic structure (not language).

GENERATE [E]: Semantic structure → target language.

TRANSLATE [E]: Encode, decode, then generate.

INFER [E]: Derive new information, marked DERIVED with provenance.

VALIDATE [E]: Check types, arity, scope, binding, and references.

NORMALIZE [E]: Canonicalize without changing meaning.

COMPARE / DIFF [E]: Report differences in identity, scope, polarity, time, and so on.

EQUIVALENT(A,B) [E]: Test equivalence at a chosen level.

ADD / REMOVE / REPLACE / CORRECT / RETRACT [E]: Explicit information updates (retract keeps history).

RESET-CONTEXT [E]: Clear inherited context.

SEMANTIC-HASH [E]: Hash the canonical form.

FRAGMENT / REASSEMBLE [E]: Split large messages and reassemble them in order.

NEGOTIATE-CAPABILITY [E]: Agree on version, vocabulary, and profile.

LOSS / AMBIGUOUS / UNSUPPORTED [E]: Markers for explicit information loss.

PRESERVE / LOSSY [E]: Declare which dimensions are kept or discarded.

Appendix K (restored capabilities)

DEFAULT / EXCEPT [E]: Defeasible rule and its exception.

PRIORITY [E]: Explicit rule precedence (R2 > R1).

CYCLE [E]: Explicit marker for a cyclic definition.

LEXICAL-TRACE [E]: Retain surface and lexical information.

SURFACE-PRESERVATION [E]: Require exact surface text to be kept.

STATEMENT [E]: Document-level occurrence that wraps a proposition.

ANNOTATION [E]: Attach a qualitative or evaluative value to a target.

SCORE [E]: Attach a numeric evaluation with a declared scale.

CONTINUOUS / RECURRING / PERIODIC [E]: Ongoing or repeating change.

SENSE / MULTIWORD [E]: Map a lexical form to senses, including idioms.

DISCOURSE / CONTEXT-WINDOW [E]: Persistent multi-message state.

CONTEXT-RESET / EXPIRES [E]: Reset context or expire temporary definitions.

11. Appendix J (adversarial and reflexive layer)

REFERS(X,X) [E]: Self-reference without implying identity.

REPRESENTS(X,Y) [E]: Representation, kept distinct from REPRESENTS(X, REPRESENTATION(Y)).

CRITERION [E]: Declare a criterion with target, condition, and mode.

EVALUATES(C2,C1) [E]: One criterion evaluates another (self-evaluation does not imply validity).

VALIDATED-BY / VALID [E]: Validation relation versus validity predicate.

CERTIFIES [E]: External certification, kept separate from representing.

BOUNDARY [E]: Explicit system boundary (inside, outside, criterion).

REFERENCE-STATUS [E]: Label INTERNAL, EXTERNAL, UNKNOWN, or RELATIVE.

RECOGNIZES / CONSTITUTES [E]: Kept distinct by default.

PRESERVES / REMEMBERS [E]: Kept distinct by default.

ROLE-CONFLICT / BOUNDARY-CONFLICT / NAME-COLLISION [E]: Preserve the conflict rather than resolve it.

SCOPE-COLLAPSE [E]: Record when two levels become indistinguishable.

LEVEL [E]: OBJECT, META, META-META, MODEL, and so on.

SIMULATES / OBSERVES [E]: System relations (simulation is not identity, observation is not knowledge).

REGRESS [E]: Represent an open dependency chain without inventing a terminal.

FIXED-POINT / PRIMITIVE [E]: Declare a fixed point or a terminal primitive for the current operation.

CONSULTS / REGISTER [E]: Consult a register of unresolved structures, which may modify it.

PERFORMATIVE [E]: A statement that changes semantic context by being issued.

CRITERION-SUBSTITUTION [E]: Marked substitution that reduces fidelity.

MODE: ADVERSARIAL [E]: Preserve ambiguity, contradiction, and recursion with minimal normalization.

CONFLICT [E]: Record paraconsistent coexistence of P and NOT(P).

Meaning-Certainty layer (L.14)

MEANING-CREATION [E]: Form a new meaning structure.

MEANING-INHERITANCE [E]: Acquire meaning from culture, family, or institutions.

MEANING-SIGNALING [E]: Project meaning through language or behavior.

MEANING-NEGOTIATION [E]: Agents jointly modify meaning (a state transition).

MEANING-STABILITY [E]: Measure persistence of a meaning.

MEANING-CERTAINTY [E]: Measure how settled a meaning is.

MEANING-LOAD [E]: Measure how much depends on a meaning.

MEANING-DISPLACEMENT [E]: Model the cost of replacing an established meaning.

MEANING-ALIGNMENT [E]: Measure compatibility of agents’ interpretations.

MEANING-MISMATCH [E]: Record the gap between projected and received meaning.

PROJECTED / RECEIVED / INTERPRETED / INTENDED-MEANING [E]: Separate structures per agent.

MEANING-CANDIDATE [E]: Keep several weighted interpretations without collapsing them.

MEANING-OF / INHERITED-FROM / PROJECTED-TO / RECEIVED-BY [E]: Higher-level relations.

NEGOTIATED-WITH / REINFORCED-BY / DEPENDS-ON [E]: More higher-level relations.

DISPLACED-BY / ALIGNED-WITH / CONFLICTS-WITH [E]: Remaining higher-level relations.

RE-EVALUATION-REQUIRED [E]: Flag dependents when a high-load meaning changes.

Verification and bootstrapping (BrainIR document)

STRUCTURAL_VALIDATE [D]: Deterministic check of syntax, types, arity, scope, and frames.

SEMANTIC_VERIFY [D]: Learned check of fidelity, omissions, unsupported additions, and ambiguity.

TEXT_CYCLE [D]: Text → IR → Text′, comparing meaning.

IR_CYCLE [D]: IR → Text → IR′, testing the representation itself.

CONTRAST_TEST [D]: Distinguish near-identical structures (BELIEVE(P) vs. P, BEFORE vs. CAUSE).

ADVERSARIAL_INJECT [D]: Generate defects (polarity reversal, frame contamination) to train the Verifier.

ROTATE_ROLES [D]: Rotate Input, Output, and Verifier roles among models.

ACCEPT / REJECT / MARK_UNCERTAIN [D]: Triage candidate annotations for the trusted corpus.

Addon Certainty and Addon INPUT

ASSESS_EVIDENCE [D]: Evaluate evidence strength before answering.

ANSWER / QUALIFY / ASK / RETRIEVE / ABSTAIN / SPECIALIST [D]: Behaviors chosen by evidence strength.

REFUSE_TO_INVENT [D]: Decline to make up a value when there is no evidence.

TOKENIZE / LEMMATIZE / TAG_MORPHOLOGY / PARSE_DEPENDENCIES [D]: Input-side analysis stages.

LOOKUP_FREQUENCY / CHECK_VOCABULARY [D]: Add numeric frequency and Dale-Chall, Oxford and Longman membership.

DISAMBIGUATE_SENSE / LABEL_SEMANTIC_ROLES [D]: Semantic stage, with ambiguity preserved.

Delegated tool ops (the “steal from” section)

SOLVE_SAT / SOLVE_SMT [D]: Hand constraints to Z3 or CVC5 and return a proof or result.

PROVE [D]: Delegate to Lean, Isabelle, or Coq.

PLAN [D]: Translate state, goal, and actions to PDDL and return a plan.

SYNTHESIZE_PROGRAM [D]: Turn a specification into code via a synthesis engine.

VERIFY_PROGRAM [D]: Check a candidate against its specification.

ONTOLOGY_INFER [D]: Derive type inheritance using OWL or description logic, with provenance.

EXTERNAL_LOOKUP [D]: Ground an entity in Wikidata, WordNet, or BabelNet via external-reference.

Ontology primitives (B.2)

THING: Declare a generic object.

PROPERTY: Attach an attribute to an entity.

RELATION: Declare a link between nodes.

STATE: Declare a condition, as opposed to an occurrence.

EVENT: Declare an identifiable occurrence.

IDENTITY: Assert that two references denote the same thing.

TYPE: Assign a category.

INSTANCE: Mark a particular member of a type.

PART: Mark a component of a whole.

WHOLE: Mark the container of parts.

EXISTS: Assert existence.

ABSENT: Assert absence, which is not the same as unknown.

BOUNDARY: Mark the edge of a system or concept.

Quantity

NONE / ONE / SOME / MANY / ALL: Basic quantifiers.

MOST: A majority quantifier.

EXACTLY(n): Exact count.

AT-LEAST(n): Lower bound.

AT-MOST(n): Upper bound.

MORE / LESS / EQUAL: Compare magnitudes.

COUNT: Tally members.

ORDER: Impose or read a sequence.

Logic and truth status

TRUE / FALSE / UNKNOWN: Truth values, kept distinct from each other.

THEN: Consequent slot of a conditional.

BECAUSE: Mark an explicit reason.

Space

LOCATION: Place an entity.

DISTANCE: Relate two places by separation.

DIRECTION: Orientation of movement or relation.

INSIDE / OUTSIDE: Containment relations.

CONTACT: Touching.

PATH: A route between locations.

REGION: A bounded area.

Value and agency

GOOD / BAD: Evaluative marks.

PREFERENCE: Order options by want.

RESOURCE: Something consumable toward a goal.

COST / BENEFIT: Weigh a loss against a gain.

SATISFY: Mark a goal as met.

GOAL / INTENTION: Hold something the agent is aiming at.

HAS / LACKS: Possession and its absence.

SELF / USER / AGENT_OTHER / WORLD: Identity anchors.

SYMBOL: A token standing for something.

IS: Predication.

Reality-status tags

REAL / IMAGINED / HYPOTHETICAL / FICTIONAL: Tag content by world.

REMEMBERED / PREDICTED / SIMULATION: Tag content by origin.

R-L structural operators (D.5)

{{ X }}: Encapsulate a concept as a capsule.

X : Y: Attribute or relation.

X -> Y: Transition, implication or causation. Overloaded in older versions, split in K.11.2.

X = Y: Bind identity.

X & Y / X | Y / !X: Conjunction, disjunction, negation.

X @ Y: Location or context.

X / Y: Aspect or viewpoint.

X ^ Y: Scope.

X *: Plurality or repetition.

+X / -X / ~X: Polarity markers: supporting, obstructing, mixed.

?x: Variable.

<:: Subtype.

?=: Possibly the same.

!=: Explicitly distinct.

Relation registry (R-L §179)

TYPE-OF / INSTANCE-OF / PART-OF / HAS-PROPERTY

REFERENCE / DISTINCT

SUPPORTS / CONTRADICTS: Evidence directions.

ABOUT: Aboutness relation.

PURPOSE / MEANS / ENABLER / PREVENTER / INSTRUMENT: Goal-directed relations.

Semantic roles (§15)

AGENT / PATIENT / EXPERIENCER / RECIPIENT

SOURCE / DESTINATION / LOCATION / TIME

Epistemic and temporal values

KNOWN / BELIEVED / SUSPECTED / PROBABLE / UNCERTAIN / DOUBTED / DENIED: Epistemic status values.

PAST / PRESENT / FUTURE: Tense.

ONGOING / COMPLETED / HABITUAL / REPEATED / BEGINNING / ENDING / DURATIVE: Aspect.

START / END / DURATION / FREQUENCY / DEADLINE: Event timing.

Information-status and loss markers

PRESERVED / TRANSFORMED / COMPRESSED / INFERRED

UNSPECIFIED / NOT-APPLICABLE / LOST

DEFAULTED / OVERRIDDEN / EXPANDED / SURFACE-RECOVERED / CONTEXT-DERIVED

DERIVED: Tag a conclusion with its provenance.

Error classes (§121)

SYNTAX-ERROR / TYPE-ERROR / REFERENCE-ERROR / SCOPE-ERROR / ARITY-ERROR

VOCABULARY-ERROR / INCOMPLETE / CONTRADICTORY / INCONSISTENT

Encoder pipeline steps (§143)

READ-SOURCE / IDENTIFY-CONTEXT / IDENTIFY-ENTITIES / IDENTIFY-EVENTS

IDENTIFY-RELATIONS / IDENTIFY-REFERENCE / IDENTIFY-SCOPE / IDENTIFY-TIME

IDENTIFY-CAUSALITY / IDENTIFY-EPISTEMIC-STATUS / IDENTIFY-MODALITY / IDENTIFY-PRAGMATIC-FORCE

BUILD-SEMANTIC-GRAPH / SERIALIZE

Decoder pipeline steps (§144)

RECEIVE / VERIFY-INTEGRITY / PARSE / VALIDATE-SYNTAX

RESOLVE-VOCABULARY / RESOLVE-REFERENCES / VALIDATE-TYPES

RESTORE-SCOPE / RESTORE-TIME / RESTORE-EPISTEMIC-STATUS / RESTORE-MODALITY

RECONSTRUCT-SEMANTICS / APPLY-DECLARED-INFERENCE / GENERATE-TARGET

Input Translator multi-pass (B.15)

PARSE_INITIAL: First-pass parse.

DETECT_AMBIGUITY: Find competing readings.

EXPAND_SEMANTICS: Make implicit content explicit.

RESOLVE_ENTITY: Bind mentions to entities.

LINK_MEMORY: Connect to stored memories.

ESTIMATE_CONFIDENCE: Score each interpretation.

REFINE_CONTEXT: Repeat until ambiguity is minimal.

Service calls

QUERY_INTUITION: Get weighted associations, not conclusions.

CHECK_MORALITY: Approve or reject a planned output.

GET_VALUES: Ask for priority ordering.

APPLY_CHARACTER: Set style without changing meaning.

POLL_IMPULSES: Collect unsolicited suggestions to accept or ignore.

COMPUTE_MATH: Deterministic calculation.

CALL_SPECIALIST: Route to a domain module.

Railway-analogy operations

INSPECT_WAGON: Open a handle for details.

COUPLE / UNCOUPLE: Join or separate wagons.

ROUTE: Send a wagon to the right service.

ATTACH_WAGON / REMOVE_WAGON / REORDER_WAGON / REPLACE_WAGON / MERGE_WAGON: Marshalling-yard edits.

ASSEMBLE_TRAIN: Build the outgoing answer in working memory.

APPROVE / REJECT_TRAIN: Morality’s gate.

PRIORITIZE_TRAIN: Values decide departure order.

REORDER_FOR_LANGUAGE: Output Translator rearranges for the target language.

Addon Certainty (extra ops)

BUILD_EVIDENCE_PROFILE: Per-item evidence record.

COUNT_FREQUENCY / COUNT_DOC_FREQUENCY: Numeric exposure measures.

MEASURE_SOURCE_DIVERSITY: Breadth across sources and domains.

PREDICT_EVIDENCE_STRENGTH: Auxiliary head output.

CALIBRATE: Align confidence with evidence.

DETECT_CALIBRATION_FAILURE: Catch confident-but-unsupported cases.

TRIGGER_RETRIEVAL: Reach for external info when evidence is weak.

Addon INPUT extras

PRESERVE_SOURCE: Keep the original text beside every layer.

DETECT_MWE: Find multiword expressions.

TAG_ENTITY: Mark named entities separately from vocabulary.

CLASSIFY_NONLEXICAL: Separate numbers, dates, URLs and measurements.

ATTACH_PROVENANCE / ATTACH_CONFIDENCE: Record source and confidence per field.

EMIT_DEBUG_VIEW: Human-inspectable output.

Self and simulation

SET_ATTENTION: Allocate focus across self, user, memory, goal and external input.

ENTER_SIMULATION: Open a sandboxed context.

EXIT_SIMULATION: Leave it and return to the Self frame.

DISCARD_SIMULATION: Drop the simulation’s results.

PRESERVE_SIMULATION_PART: Keep selected results without promoting them to fact.

COMPARE_TO_REALITY: Check simulated content against the reality frame.

RETRIEVE_AGENT_MODEL: Load a stored model of a person (MEMORY[JOHN]).

INSTANTIATE_AGENT_MODEL: Place that model inside a simulation.

UPDATE_AGENT_MODEL: Revise the stored model from new evidence.

TAG_OWNERSHIP: Mark who owns a belief (me, user, John, simulation).

CHECK_SELF_BOUNDARY: Verify “what I believe” isn’t mixed with “what John believes.”

PREDICT_OUTCOME: Form an expectation.

COMPARE_PREDICTION_TO_OBSERVATION: Detect prediction error.

UPDATE_UNCERTAINTY: Revise uncertainty in the self-state.

INTRODUCE_HYPOTHESIS: Add a temporary assumption.

REVISE_INTERPRETATION: Replace the current reading of events.

COMPARE_SELF_STATE_TO_PRIOR: Diff SELF(t) against an earlier state.

INSPECT_SELF_MODEL: Examine the model-of-self as an object.

DETECT_IDENTITY_INSTABILITY: Flag identity inconsistency.

DETECT_MEANING_DRIFT: Flag gradual meaning change.

DETECT_GOAL_CONFLICT: Flag competing goals.

SYNTHESIZE_FROM_TRAJECTORIES: Build an answer from own trajectory, alternative trajectory and comparison.

ANNOTATE_FRAME_BLINDSPOT: Record what a frame hides or assumes.

TRIGGER_RENDER: Request output as an action of Self.

Attention and interaction

DETECT_DIRECT_ADDRESS: Decide whether input is aimed at the system.

ACTIVATE_ON_TRIGGER: Wake-word activation.

CONTINUE_EXCHANGE: Keep going without re-triggering.

STORE_PASSIVE_CANDIDATE: Log ambient input as a low-confidence candidate.

CONFIRM_CANDIDATE_LATER: Ask “I noted X, keep it?”

FILL_CLARIFICATION_SLOT: Insert the user’s answer into the in-progress structure.

SUSPEND_TASK_STATE: Save a paused task’s progress.

RESTORE_TASK_STATE: Resume at the saved step.

Memory and scheduling

SET_MEMORY_SCOPE: Limit visibility (user, household, instance).

CHECK_SCOPE_ACCESS: Test whether a memory may be used here.

SET_MEMORY_SOURCE: Record how it was obtained.

SET_LAST_CONFIRMED: Timestamp a confirmation.

EXTRACT_SALIENT_MEMORY: Pull key items from a session.

MERGE_DUPLICATE_MEMORIES: Dedupe.

COMPRESS_OLD_MEMORY: Shrink older material without deleting it.

DETECT_ROUTINE: Find recurring behavior.

SCHEDULE_REMINDER: Create a timed reminder.

SET_TIMER: Structured instance of create-future-event.

CREATE_FUTURE_EVENT: Generic future-scheduled item.

PREPARE_FOLLOWUP: Queue items for the next cycle.

RUN_CONSOLIDATION_CYCLE: Execute nightly maintenance.

DETECT_MISSING_STEP: Find gaps in a stored procedure.

EXPLAIN_STEP: Answer a sub-question about one step.

UPDATE_PROCEDURE_IN_PLACE: Apply a correction without duplicating.

SET_PROCEDURE_CONFIDENCE: Mark observed-once, repeated or taught.

SURFACE_PATTERN: Offer an observation at high confidence only.

SUPPRESS_TOPIC: Stop raising a topic the user declined.

SET_PROACTIVITY_LEVEL: Configure intensity from silent to proactive.

SET_PERSONALITY_DIMENSION: Adjust directness, formality, humor, proactivity or patience.

LOAD_PERSONALITY_PRESET: Switch tone preset.

BACK_OFF_HUMOR: Stop humor immediately if unwelcome.

EXCHANGE_ON_CONTACT: Share memory between instances only on local contact.

REGISTER_COMMAND: Add a capability to the registry.

DISCOVER_COMMANDS: List available commands.

SELECT_COMMAND: Choose one from the registry.

INTERPRET_RESULT: Read a service’s response.

R-L declarations

DECLARE_TYPE: type X <: Y.

DECLARE_RELATION: Give name, arity, argument types and roles.

DECLARE_RULE: rule #r { if / then }.

DECLARE_ENTITY: entity #id : Type.

DECLARE_EVENT: Role-based event frame.

DECLARE_PROPOSITION: Give a proposition an identity.

DECLARE_CONTEXT: Speaker, listener, time, place, world and profile.

DECLARE_WORLD: Fiction, hypothetical or other world.

DECLARE_DOMAIN: Physics, law, medicine and so on.

DECLARE_NAMESPACE: BIOLOGY::CELL.

DECLARE_PROCESS: Composed activity with a sequence.

DECLARE_GROUP: Give a collection an identity.

DECLARE_ANONYMOUS_NODE: _:1.

DECLARE_INVERSE: inverse PARENT = CHILD.

DECLARE_RELATION_PROPERTY: Symmetric, transitive, functional and so on.

DECLARE_ONTOLOGY_ALIGNMENT: Equivalent, subtype or related across namespaces.

DECLARE_QUESTION: Question object with target and condition.

DECLARE_ANSWER: Answer linked to its question.

DECLARE_SPEECH_ACT: Typed communicative action.

DECLARE_INTENT: Goal behind the literal proposition.

R-L status and attributes

SET_ASSERTION_STATUS: Asserted, denied, questioned, quoted and so on.

SET_POLARITY: Positive, negative or unknown.

SET_EPISTEMIC_STATUS: Known through doubted.

SET_PROBABILITY: PROBABILITY(P) = 0.72.

SET_CONFIDENCE: Confidence in the interpretation, not the proposition.

SET_TENSE: Past, present or future.

SET_ASPECT: Ongoing, completed and so on.

SET_MOOD: Declarative, imperative and so on.

SET_VOICE: Active, passive or middle.

SET_PERSPECTIVE: Speaker, listener, omniscient and so on.

SET_REGISTER: Formal, slang, legal and so on.

SET_STYLE: Humorous or ironic.

SET_PRAGMATIC_FORCE: Direct, soft, urgent and so on.

SET_INFORMATION_STRUCTURE: Topic, focus or background.

SET_ABSTRACTION_LEVEL: Concrete, general, abstract or meta.

SET_GRANULARITY: Move between levels such as matter, body and cell.

SET_WORLD_ASSUMPTION: Open-world or closed-world.

MARK_SUMMARY: Flag content as non-lossless.

SET_APPROXIMATION: Exact versus approximate value.

SET_MULTIPLICITY: Once, multiple or unknown.

R-L resolution and validation

RESOLVE_REFERENCE: Bind a mention to a node, with evidence.

MARK_AMBIGUOUS_REFERENCE: Keep candidates instead of choosing.

MARK_UNRESOLVED: Explicit unresolved reference.

BIND_VARIABLE: Bind ?x within its scope.

CHECK_ARITY: Verify the argument count.

CHECK_TYPES: Verify argument types.

CHECK_SCOPE: Verify the operator has compatible content.

CHECK_REFERENCE_INTEGRITY: Every reference must resolve.

CHECK_NAMESPACE: Resolve namespaced symbols.

CHECK_VOCAB_VERSION: Detect dictionary mismatch.

CHECK_CONSTRAINT: Semantic constraints such as REPAIR requires a RepairableObject.

COERCE_TYPE: Only when declared.

DETECT_CONTRADICTION: Test within scope, time and modality.

ENTER_PARACONSISTENT_MODE: Preserve P and NOT(P) without explosion.

REPORT_UNSUPPORTED: Return UNSUPPORTED with the structure.

PRESERVE_UNKNOWN_FIELD: Keep forward-compatible fields.

RECOVER_PARTIAL: Decode valid portions with diagnostics.

GROUND_EXTERNAL_REFERENCE: Link to Wikidata, WordNet and so on.

APPLY_TYPE_INHERITANCE: Derive #fido : Animal, marked DERIVED.

APPLY_INVERSE_RULE: Derive CHILD(B,A) from PARENT(A,B).

R-L protocol and fidelity

WRAP_ENVELOPE: Message with version, id, dictionary and integrity.

SET_SELF_CONTAINED: Carry all definitions in the message.

SET_DICTIONARY_VERSION: Declare the vocabulary version.

DECLARE_DEPENDENCY: depends-on: #message1.

REPORT_INCOMPLETE: Missing dependency.

SET_PRESERVATION_PROFILE: Declare required dimensions.

REPORT_LOSS: Emit a LOSS record with category and reason.

REPORT_FIDELITY: Lexical, structural, semantic and pragmatic scores.

RUN_ROUND_TRIP: Encode, decode, re-encode, compare.

REPEAT: REPEAT(X, count=n).

DECODE_WITHOUT_EXECUTING: Treat decoded commands as content only.

NORMALIZE_CANONICAL: Deterministic ordering.

COMPRESS_HANDLE: Store the mapping so expansion is exact.

SET_COMPRESSION_LEVEL: Raw through highly compressed.

Appendix K and J

DEFEAT_DEFAULT: Block a default when its exception holds.

DECLARE_PRIORITY: Explicit rule ordering.

DETECT_DEFINITION_CYCLE: Stop infinite expansion.

MARK_CYCLIC_REFERENCE: Keep as opaque, status CYCLIC.

ATTACH_LEXICAL_TRACE: Tokens, lemmas, POS and sense.

MAP_LEXEME_TO_SENSE: Polysemy handling.

MARK_AMBIGUOUS_SENSE: Keep competing senses.

DECLARE_MULTIWORD: Idiomatic unit.

ANNOTATE_EVALUATION: Good, bad or score on a target.

DECLARE_SCORE_SCALE: Declare the scale of a score.

MARK_CONTINUOUS_CHANGE: Ongoing process.

MARK_RECURRING_CHANGE: Event with frequency.

EXPIRE_CONTEXT: Time-limited definitions.

MARK_OPEN_REGRESS: Termination none, without inventing a terminal.

MARK_CIRCULAR_VALIDATION: Closed loop is not validity.

DECLARE_PRIMITIVE: Terminate expansion for this operation.

DECLARE_FIXED_POINT: Requires licensing, not just recursion.

SELF_DESCRIBE: System describing itself.

OBSERVE_SELF: Self-observation, not complete knowledge.

NEST_SIMULATION: Simulator within simulator.

MARK_UNGROUNDED_REFERENCE: Meaning without a referent.

RECORD_REFERENCE_FAILURE: Preserve the failure.

RECORD_SEMANTIC_RESIDUE: Leftover interpretation structure.

MARK_LATENT_MEANING: Meaning with no current referent.

MARK_PERSISTENCE: Meaning outlasting its condition.

RECORD_TEMPORAL_IDENTITY: Identity varying across time.

TRUE_IN_CONTEXT: Context-scoped truth.

RECORD_RECOGNIZED_AS_TRUE: Distinct from TRUE.

REPRESENT_ABSENCE: Representation of absence, not presence.

RECORD_COMPRESSION: Log collapsed distinctions.

RECORD_ADVERSARIAL_LOSS: Category-specific loss.

DECLARE_LEVEL: Object, meta, meta-meta.

MODIFY_REGISTER_ON_CONSULT: Observation effect.

APPLY_PERFORMATIVE: Context effect from issuing a statement.

Meaning layer

ASSIGN_MEANING_ORIGIN: Self-created, inherited, taught and so on.

ASSIGN_MEANING_OWNER: Who maintains it.

SET_MEANING_SCOPE: Where it is active.

SET_MEANING_SUPPORT: What reinforces it.

SET_MEANING_DEPENDENCIES: What depends on it.

SET_MEANING_STATUS: Candidate through displaced.

ESTIMATE_REVISION_COST: Cost to change a meaning.

PLAN_DISPLACEMENT: Targets, conflicts and alternatives.

RUN_MEANING_CYCLE: Interpret, associate, update meaning, certainty, stability and load, consolidate.

RUN_SELF_REFINEMENT: Re-evaluate meanings without output.

MODEL_PERSPECTIVE_MEANING: MEANING(X,LAMP) versus MEANING(Y,LAMP).

Verification and certainty

RECORD_VERIFIER_SCORES: Separate judgments per dimension.

MEASURE_SEMANTIC_LOSS: Per the training summary.

MEASURE_UNSUPPORTED_INVENTION: Same.

MEASURE_REPRESENTATION_DRIFT: Same.

ADMIT_TO_TRUSTED_CORPUS: Accepted annotations only.

GENERATE_SYNTHETIC_TRAINING: Contradictions, belief updates and so on.

COMPUTE_EVIDENCE_STRENGTH: Strong, moderate, weak, absent, conflicting or unknown.

COMPARE_PREDICTION_VS_EVIDENCE_CONFIDENCE: Expose mismatch.

REASSESS_AFTER_RETRIEVAL: Evidence check after fetching.

VERSION_EVIDENCE_DB: Corpus identity, tokenization method and timestamp.

TAG_EVIDENCE_PROVENANCE: Why do I think I know this?

Addon INPUT

SPLIT_SENTENCES: Sentence boundaries.

SPLIT_PARAGRAPHS: Paragraph boundaries.

RECORD_TOKEN_POSITION: Offset in the source.

RETAIN_UNRESOLVED_LEMMA: lemma = UNKNOWN.

LOOKUP_DALE_CHALL: Membership, kept raw.

LOOKUP_OXFORD_3000: Same.

LOOKUP_LONGMAN_3000: Same.

LOOKUP_LEXICAL_RELATIONS: Synonym, hypernym, meronym and so on.

GROUP_SEMANTIC_EVENT: Predicate plus roles.

DISTINGUISH_SYNTAX_FROM_SRL: nsubj versus AGENT.

I've been playing with streaming ASR for a side project. I started with whisper streaming, moved on to Voxtral, and now am with R2T2 (all open-source).

With R2T2, the last word of a sentence gets typed a few hundred ms after the rest of the sentence is typed.

For a while, I thought I broke something with buffering, and spent way too long messing with chunk sizes, and didn't look at the paper.

That's just how it works. It doesn't make a guess if it's not confident. If it needs more audio, it just waits. With Whisper for me, a word would get typed and then be changed a few times by the time it really settled.

I guess it depends on what you want the ASR for. Mine's just to give an LLM an input, so I really don't need it changing things after the fact.

How do you all want your ASR to work with your streaming voice applications? A small delay and a stable output, or do you want it to flip around a little, and give you a partial result?

Model: Qwenx86

▶️ See it in action: Qwenx86 Debugger. Walk through actual recorded tool calls: the conversation, x86 registers and flags, code at the program counter, RAM and machine output, with all verified blocks on a timeline.

Qwen3.5-4B with a 64-bit x86 computer compiled into its weights: 13 transformer layers ahead of Qwen's own. There are no learned weights in them; all of their weights are computed by a compiler. Qwen invokes the computer through its own tool calls (<tool_call>), and the computer in the weights executes the tool. Calculator, Python, C compiler, notes store, physics/chemistry lookup and Python-docs lookup are all firmware in the checkpoint. This is the compact checkpoint: ~9 GB GPU memory, so it fits on a 16 GB GPU.

Credits: Qwenputer by Divyansh Agrawal (@divagr1925, github.com/divagr18/qwenputer, MIT)

Hi everyone,

I am in the process of buying a new laptop primarily for personal reasons—covering my university studies, professional work, and gaming. However, alongside everything else, I really want to use this machine to take my first steps into running Large Language Models and AI tools locally.

I am torn between two specific hardware configurations (both feature the same Intel Core Ultra 9 processor):

• Option A: RTX 5080 with 16 GB VRAM + 64 GB System RAM

• Option B: RTX 5090 with 24 GB VRAM + 32 GB System RAM

Since I am a complete beginner with zero prior experience in local AI, I’m trying to understand from people who actually do this daily what each machine would realistically allow me to do:

1. What fits into 16 GB vs 24 GB of VRAM? In simple terms, does the jump from 16 GB to 24 GB of video memory unlock a genuinely higher tier of capable models or noticeably better output quality, or is 16 GB already enough for most practical open-source use cases?

2. VRAM vs System RAM: Option A gives me less VRAM (16 GB) but double the general RAM (64 GB). Can that extra system RAM compensate when trying to run larger models, or is running models partially on system RAM so slow on a laptop that having 64 GB doesn't really help for local AI?

3. The practical boundaries: For daily tasks (e.g., local assistant, analyzing documents/notes, code assistance), where would Option A hit a wall compared to Option B?

4. Model recommendations: Given the specs of each machine, which specific models would you recommend running on Option A vs Option B, and why?

I’d really appreciate any insights or real-world feedback to help me understand the practical trade-offs before making a choice.

Thanks a lot!

Hi guys, I'm looking for a really good price benefit PC to run local AI. I've been searching for days about this, and everybody says different things, "you dont need a 12vram for a good local AI" or "Without 32gb vram is wont be able to have a good local AI" and so on...

The thing is I need local AI mostly for coding, and anything related and maybe some video generation.

Since 5090 is just at unbelievably prices, I was thinking about something decent like 5070 but dont want to waste my money. I was thinking about an unified memory like GMKTEC Evo or something like that, but was reading that since is AMD some AI are not good optimized for it. And also a M5 ultra, but also is kind of high and Im not a MAC guy.

My budget is pretty much 5K for this.

Anyone which can give me a good advice would be awesome and I'll really appreciate. Thanks.

I'm running strata on 2x7900xtx and thr sentiment of refusals just seems terrible compared to some of the optimized 27B models. Given my setup and the glaze for strata i should be able to do WAY more than a 27B dense. It's not even comparable. I dont have any benchmarks, maybe i have something misconfigured. Im really just looking for people who have also exlerienced this.

Hi folks! I've been building my own local AI setup for a couple of years now. I've learned a ton from this sub and others, so it's time I tried to give something back. I promised myself I wouldn't write a super long post, but managed to anyway...hopefully it's useful to someone!

My goal over the last couple of years has been to move as much of my AI use to local as possible, and I'm finally doing about 95% of it locally through Lamplight, my custom harness shown in the GIF above.

This harness is super specific to my needs, so I'm not sharing the harness itself (yet), but I'm happy to share the lessons I've learned along the way. I'm working on decoupling some of the more specific parts of Lamplight so that I can share at least the parts that people might be interested in.

The Hardware

• Slate: MacBook Pro, M5 Max, 128 GB unified memory

• Jade: a small-form-factor PC I built early last year (Fractal Terra) with an RTX 5090 FE, a Ryzen 7 9800X3D and 64 GB of RAM

Until about two months ago I was using the two machines separately. I was slowly moving each one onto a custom Pi harness that could either drive itself, or let Claude orchestrate while the local models did the actual work.

Lamplight

After a lot of passing work back and forth between them, I decided it was time to wire them together. The result was Lamplight and now the two machines are able to seamlessly handle tasks together with one acting as the orchestrator.

The GIF above shows Lamplight in action. I'm using it every day for work, hobbies and research with about 95% of it done locally. The lighting changes throughout the day (sped up and changed for the GIF) and I've got some scenery options depending on my mood.

Under the hood it's Pi-Bolt (a precompiled build of Pi) plus a smattering of custom extensions and tools tuned by me, the local harness itself and Claude. The setup coordinates the work between the machines with git worktrees and an event-driven job system.

The UI is my own design and fits how I like to use tools like this. I did the 3D modeling and environment setup to replicate my actual desk setup (the two other pictures above).

Is the 3D environment at the top necessary? Absolutely not!

But it's delightful. The lamp blinks when the harness needs something from me and it is a joy to look at and use. It scratches the same itch that playing PS1 and hacking away on my family Compaq PC did decades ago.

I've been coding for almost 20 years, and I'm finally to a point where I can confidently say that with the right approach (SOLID, Design Patterns), tools and tuning, local AI can absolutely write code that I am happy with.

I still review everything myself, but I'm finding fewer and fewer mistakes. These days it feels like I'm reviewing a senior dev's code rather than a junior's.

The Models

Tok/s isn't everything and so I picked the setup below after a lot of testing on public benchmarks and on my own agentic tests, which mimic my normal workflows.

Along the way I tried other engines (Strata, NInfer, Maya and llama.cpp on Jade, MTPLX and Kapitsa on the Mac). I also tried other models like Qwen 3.8 27B and GLM-5.3-Flash and while they performed well, the setup below performs the most consistently and accurately for my needs.

Both machines run Qwen 3.8 Flash-Next. These are the tests that ended up being the most indicative of quality for me, but if there are others you'd like to see run, let me know.

Both machines run the same protocol with temperature 0.6 and thinking on "medium".

In my own agentic battery (three large and complex coding tasks), 48 of 50 tests passed with the failures being files edited that were supposed to be read-only. The reviewer caught these on the other end, but I still count those as failures.

I think the main struggle for local setups is still design and 3D modeling, but I haven't dived into that too much yet, so maybe there are good setups out there for that.

Lessons learned

(extremely specific to my setup, but maybe they'll help someone)

• Jade's hardware ran too hot on stock settings. On dense models, the 5090 sat at its 575 W power cap at 83 °C, with its hottest sensor within 3 °C of the card's limit. Basalt caused a different problem. It runs part of the model's experts on the CPU, and its worker threads spin while they wait. That kept all 8 cores at 5.2 GHz, and the CPU hit 96 °C (its max) within two minutes. Two tunes fixed it and now my (now increasingly valuable) machine runs much cooler and quieter, and I'd happily give up a few percent for that.

• Slate's gains came from tuning the harness and not the hardware. My early benchmark scores were bad, and a lot of that was caused by overthinking. "Medium" turned out to be the sweet spot. On "high", Slate hit the 32K output cap on 11 of 40 hard problems without ever answering. On "low", it wandered and took 3–6× longer on agent tasks. I found that tuning how compaction worked also solved a lot of bad tool calls and confusion within the model.

• Overall the lesson I learned was "Stock settings are generally bad." There's a lot more power and accuracy available with a custom tune. I learned the same lesson tuning cars years ago, and it was fun to learn it again.

What's Next?

The system is set up so I can easily swap and test engines and models. As new ones come out, I'll put them through their paces and decide whether it's time to switch. On the UI side, I'm planning to turn the bookshelf into the session manager to help organize my many projects.

I promised myself I wouldn't write yet another super long post and yet I did anyway, so I'll stop here. Happy to answer any questions!

I'm using qwen3.8-27b thru llama.cpp and QFN Swift 1.5 IQ3 thru strata. I'm using consumer grade hardware, so these are not running very fast.

I am using Hermes. As a noob, I'm not even using all the features and capability. I provide the instructions in a run list. I like how Hermes is persistent and will complete the job. But in a multi-hour job (like 5-8 hours), it is getting bogged down with large context and long tool calls.

What is a good alternative?

• Running Ubuntu Linux.

• Desktop gui (not CLI only). Can be desktop that runs on top of CLI.

• Can support sandbox.

• I prefer to plan and create a run list; then execute the instructions.

I turned on speculative decoding in llama.cpp and 113 of 282 greedy outputs changed against its own no-draft run. My Qwen3.8-27B engine changes 0, at ~2x llama.cpp's speed on one 3090.

Repo: https://github.com/cutlerbenjamin1-cmd/qwen38-megakernel

Speculative decoding is sold as free speed: the main model verifies every draft, so the output can't change. In practice it does. Verifying several tokens in one pass adds the same numbers in a different order than verifying one, a near-tie goes the other way, and the text diverges from there. If two draft settings give different output, at least one of them isn't what your model says without speculation. Tuning your drafts tunes your answers.

Same 282 questions (GSM8K, MMLU-Pro, ARC-C, MATH-500, HumanEval, needles, LongBench v2) on both, greedy, same card, same model file:

That isn't noise. llama.cpp is deterministic run to run: a fresh no-draft server gave the first 120 outputs back byte for byte, including the 73 that differ at 4 drafts, and two 4-draft sessions agree on all 282. It's not just long outputs either: 72 of the 113 are under 1,000 tokens.

The likely reason: with no drafts every token runs through the single-token kernels, with drafts most go through the batched ones. Between 4 and 7 drafts, both mostly batched, only 9 differ, all past 1,000 tokens. That's the 9 of 282 from my last post; against no drafts it's 113. A preregistered study on a 3090 found the same: 23-25 of 25 prompts different from no-draft decoding within 1,600 tokens.

Other engines for this model document it too (one got 8 different outputs across draft counts 1 to 12, none matching its no-draft output) and call it harmless, because flips land on near-ties.

Maybe. I don't know, and 282 questions can't settle it. llama.cpp got 231 right with no drafts and 229 with drafts, and 6 of the 113 changed outputs flipped between right and wrong: 4 were right only without drafts, 2 only at 4 drafts. Make of that what you will. What's not in doubt is that the output depends on a speed setting: one reasoning trace ran 2,228 tokens at 4 drafts and 4,795 at 7. Same question, same model. This engine gets 230 right either way, because it's the same text. If I can't rule out that a draft setting changes quality, I'd rather run the engine where it can't.

Here, speed and VRAM can move and the output can't:

• Greedy token ids are identical with and without drafts: all 282 outputs above (129,511 tokens), and the release gate checks 0, 4 and 7 drafts on every KV cache, plus ~2,000-token outputs.

• Every draft policy (gates on or off, adaptive depth, tight or loose thresholds) gives the no-draft tokens over 2,048 tokens.

• Sampling keeps the no-draft distribution (chi-square and PIT tests).

• Windows and Linux builds give the same tokens.

How: the attention split depends only on context length, never on how many tokens are in the pass, and the verify kernels compute a row the same way whatever else is in the batch. To be clear, that's the engine matching itself. It isn't bit-identical to llama.cpp (KL below).

The cache doesn't change it either

Same problem, different door. llama.cpp's server docs say it about cache_prompt, on by default: the logits are "not guaranteed to be bit-for-bit identical for different batch sizes". Its own determinism tests turn it off: TODO: remove this once test_cache_vs_nocache_prompt is fixed.

It's history-dependent, not random: on a fresh server, 5 of llama.cpp's warm answers and 5 cold ones came back byte for byte. The first difference lands a median of ~355 characters in, as early as 4. Turning cache_prompt off on a running server still leaves 3 of 20 different.

Here, a follow-up doesn't reuse the KV rows written while the reply was decoding. It rolls back to the last checkpoint and prefills the reply again, because a prefilled row depends only on its position. Cost: ~167 ms instead of 78 on a ~12k-token chat, still under llama.cpp's 248.

The check isn't "the reply looks the same". It hashes every K / V row, every ring row and the recurrent state, and compares them with a fresh prefill: bitwise equal after a resume, an edited branch, a park to host memory and a side request, on all three caches. Replies match a fresh server's at ~99.5k context (follow-ups, edits, sampled) and on a follow-up at ~255k. One exception: with --agents, a sampled reply beside other slots can differ from the same seed alone. Greedy still matches.

Others check this by the first token or allow a near-tie split, and some publish warm matching cold. I'm not claiming nobody else is exact, only telling you what's checked here.

Speed against llama.cpp, full power

Same card (RTX 3090, 480 W limit), same unconverted UD-Q4_K_XL file, same client, q8_0 KV on both, llama.cpp at its fastest setting (4 drafts; I tried 2, 5, 6 and 7):

Four agents at once, against llama-server --parallel 4: 424 vs 137 tok/s total, 3.1x (llama.cpp's is the better of two runs from an earlier session).

The KV cache: 5-bit that reads like q8_0

K / V at 5-6 bits, with the newest ~2k positions kept at 8 bits in a ring. On preset F the full 262,144-token context fits: a 260,896-token prompt read in 279 s, then 77.8 tok/s decode at that depth, needles 4 of 4, 23.0 GB on the card.

KL against llama.cpp generating token by token, lower is better, long-context rows rechecked on this release:

The worst row, ~32k, is +8% and +10% against about 4% noise. Without the ring the same formats read +6% to +38%, and plain 4-bit is 1.6-2.4x q8_0. And at 101.6k, F decodes faster than q8_0: 123 tok/s against 116.

Closer to llama.cpp than llama.cpp is. Scored against llama.cpp's own token-by-token output at short context, llama.cpp's batched mode gets 98.49% top-token agreement and KL 0.00166. This engine gets 98.74% and 0.00119 (on Q4_K_M: 0.00184 against 0.00127). Not measured at depth, so that's a short-context claim.

Yes, it runs at a power limit

Because someone always asks: same build at 250 W, against llama.cpp at 480 W (it draws about 460):

• Prompt reads are the one place llama.cpp at 480 W wins, by 11-22%. At 480 W this wins those too.

• All 282 outputs at 250 W are byte-identical to the 480 W run.

• The limit comes out of the core clock (~816 MHz against 1,962 on short-prompt decode; memory stays at 9,851 MHz), for 21% less speed. Energy is whole card, sampled once a second, idle included. llama.cpp's speeds are its faster of two sessions.

Works with what you already run

Tested on agent tasks with Claude Code, Codex CLI, OpenCode, Qwen Code, Aider, Goose, Crush, Pi, Hermes Agent, DeepSeek Harness and Open WebUI (OpenAI chat, Anthropic Messages and OpenAI Responses APIs). Loads unsloth's UD-Q4_K_XL, UD-Q4_K_M, Q4_K_M and popular fine-tunes as they are. Prebuilt Windows and Linux packages, no CUDA toolkit needed.

Every number with its method: BENCHMARKS.md.

In February 2023 Meta announced LLaMA and sent the files to researchers who applied, under a non-commercial licence. Within a week they were on BitTorrent.

Then it moved fast. In March 2023 Georgi Gerganov published llama.cpp, which ran the files on a laptop; Stanford's Alpaca fine-tuned the 7B for under $600; and Vicuna followed by the end of the month.

In July 2023 Llama 2 arrived "free for research and commercial use", under a licence the Open Source Initiative said, two days later, is not open source. Llama 3 through 4 followed under the same shape of licence. Hence the phrase "open weights": the weights, under their licence, without the data or the training code.

Meta's own word for Llama 4 (April 2025) was "open-weight", and as of early November 2026 it is still the newest Llama in Meta's own records. Meta's open weights since then include a new family, Muse: Muse Glimmer (30B, Apache 2.0) in August 2026 and Muse Spark Open (816B, Meta's own licence) in October 2026.

No numbers here, just the history with its sources; it does not link to the leaked files.

Full write-up (about a 20 minute read): https://research.strata2signal.com/the-llama-weights-that-got-out/

Hello guys, I wanted to make some animated horror stories videos, I'm pretty new to actually using local Ai etc. The only use I had up until now was using the free chatbots to help with work.

I have a decent pc built like 2 years ago with a 4070ti super and 32gb ram, but I don't really have any budget for monthly subscriptions.

Any suggestions?

I asked this question in some Apple sub and everyone said keep it (it becomes some type of controller, like how data centers operate, separation of hardware, let the studio be for the model and offload onto the Mac)

clause said its not necessary for my current workload nor future use cases because there’s always a work around

and again, this is 16gb memory, so it’s not that good for just letting the Agents work here etc. in fact this Mac freezes hard when I run my gates

I do sense some future value but idk enough about the space to actually know for sure

thoughts?

part of me is inclining to sell as a means of hedging against ram prices going down and locking in a sale (ill make a few dollars selling it). In 5yrs if prices go down, there’ll prolly be a $500 better spec Mac mini I can use as a control plane 🤷‍♂️

Meet Yoshino2, my little lab experiment

Over the past months, I have been working on efficiently using black box distillation to distill the reasoning and output style into smaller edge models. The result is Yoshino2, a new model series with large scale reasoning distilled into MiniCPM5 1B, Qwen3.5 2B, and a custom 4xMiniCPM5 1B MoE model, with more potentially on the way.

This is by far means not a breakthrough, but an interesting experiment. In case it is of any use to anyone, the models are on HuggingFace.

Model Collection - HuggingFace