lmm

1. deeponet

   Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

   

2. AppSignal

   www.appsignal.com sponsored

   Monitoring that respects your time & budget. APM, error tracking, and dashboards for modern web apps. Ten-minute setup, transparent flat pricing, and support from engineers who actually use the product.

   

3. autogpt

   🦀 A Pure Rust Framework For Building AGI. (by wiseaidotdev)

   

4. screenpipe

   YC (S26) | Open Computer History | Record your screen continuously locally and provide context to your agents (Claude, Codex, Openclaw, Hermes, Runner...)

   

5. BACON

   Bicameral Agent with Compute-aware Orchestration and Navigation (by scottvr)

   

6. blog

   Wise AI Blog. (by wiseaidotdev)

   

7. autogpt

   Discontinued 🦀 A Pure Rust Framework For Building AGI (WIP). (by kevin-rs)

   

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better lmm alternative or higher similarity.

lmm discussion

lmm reviews and mentions

Posts with mentions or reviews of lmm. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2026-08-21.

• Genuine Intelligence will never in trillion years emerge from neural networks.

  1 project | dev.to | 21 Aug 2026

  The lmm project is my concrete, executable response to this situation. It is written in Rust, it is built around symbolic regression and physics simulation rather than around gradient descent over text corpora, and it is a deliberate proof of concept that genuine intelligence-oriented capabilities can be built without training on human creative expression. It is not finished. It is not competitive with GPT-5 on the tasks that GPT-5 is commonly used for. But it is on the right side of the architectural argument, and I will keep building it because the alternative, building nothing and only criticizing what others have built, is the most comfortable and least useful position available to me, and comfort is the one luxury I have never been able to afford.

• Knowledge and Intelligence ARE Mutually Exclusive.

  2 projects | dev.to | 21 Aug 2026

  I want to connect this to the ongoing work on the lmm project that I have been building and describing across my posts, and specifically to the lmm-agent crate that sits at its core. The lmm-agent framework is built entirely around the knowledge-intelligence distinction I have been drawing. It is an equation-based, training-free autonomous agent: no LLM API key, no token quotas, no stochastic black boxes, no weights updated by gradient descent on anyone's text. Instead of pattern retrieval, it implements what I am calling intelligence primitives, five structural properties that replace statistical interpolation with auditable, causal, and motivated cognition. These primitives are: calibrated Bayesian uncertainty via Gaussian belief propagation, compositional axiomatic reasoning that produces auditable forward-chaining proofs, causal counterfactual attribution using Pearl do-calculus interventions, hypothesis formation that ranks candidate new causal edges by explanatory power, and internalized motivational drives including distinct signals for Curiosity, CoherenceSeeking, and ContradictionResolution. Each of these primitives maps directly onto the abstract properties I described as belonging to intelligence rather than knowledge. None of them can be trained in by fitting patterns to text, because each requires a structural mechanism to be present at the architectural level, and that is exactly why I built them that way rather than trying to elicit them from a language model through prompting.

• If You Can't Build AGI, Then Why Should We Hire You?

  2 projects | dev.to | 21 Aug 2026

  I am building autogpt in Rust and the companion lmm project without gradient-based training because I want to build things that are honest about what they are. The autogpt framework makes explicit what the agent is allowed to do, through its composable architecture and YAML configurations. The lmm project is an attempt to build machine intelligence that discovers the structure of reality rather than statistically approximating the structure of human text about reality. Neither of these projects is building AGI. They are both trying to build systems that are genuinely useful for the specific things they do, genuinely reliable in the specific contexts they are used, and genuinely honest about the gap between what they do and what an intelligent human observer would do in the same situation. That is the standard I hold my own work to, and it is the standard I am advocating for in this post: not "can you build AGI" but "can you build something real that you can be honest about".

• All You Have Access To Is Knowledge and Tools; Never Intelligence!

  1 project | dev.to | 21 Aug 2026

  Causal machine learning is one of the most important and underinvested directions in AI research. The work of Judea Pearl on causal inference and do-calculus has given the field a rigorous mathematical framework for reasoning about causation rather than correlation, and researchers are beginning to extend this framework in ways that could eventually be realized in learned systems (15). Bernhard Schölkopf's group has been developing the theory of causal representation learning, which aims to learn the causal variables and their structural relationships from observational data rather than requiring explicit experimental intervention (20). These directions are hard. They require theoretical innovation at least as much as they require computational scale. They produce results that are harder to demonstrate impressively in a short demo than language model capabilities. And they are the right direction, because they are building toward systems that can genuinely understand rather than systems that can fluently retrieve. The lmm project I described in Training Is an Evil Concept. is one concrete implementation that moves in this direction, with symbolic regression for equation discovery, physics simulation, and explicit causal reasoning. It is a proof of concept, not a complete system, and I say that honestly, but proof of concept matters because it proves that the alternative exists and is engineerable.

• Training Is an Evil Concept. LMMs Eliminates it Altogether.

  1 project | dev.to | 21 Aug 2026

• the penguins are already sentient. Your neural network is just a distraction.

  1 project | dev.to | 21 Aug 2026

  I want to connect the embodied intelligence argument directly to the lmm project here, because the lmm architecture is my attempt to build toward this kind of embodied, grounded intelligence in a way that does not depend on the training paradigm I critiqued in my last post. The lmm system includes a perception layer that converts raw bytes and sensor streams into normalized tensors, a physics simulation layer that models dynamic systems using actual differential equations, a causal reasoning layer that maintains explicit structural causal models of the dependencies between variables, and a consciousness loop that ties these together by running a continuous cycle of perceiving, encoding, predicting, and acting. This architecture is closer in spirit to embodied cognition than to the standard language model architecture, not because it is biological, but because it is organized around engaging with the structure of physical reality rather than around predicting the next token in a text sequence. When you run lmm consciousness --lookahead 5, the system performs one tick of this full loop: it takes raw input, converts it to a tensor, runs a world model prediction, evaluates the prediction against the actual state, and plans an action based on the discrepancy. The output is a state vector and a mean prediction error, both of which are observable, verifiable, and grounded in the structure of the input rather than in the statistical patterns of a training corpus. That is not biological intelligence. But it is a step toward the right kind of intelligence, in a direction that the training-based paradigm cannot go, because it is organized around the world rather than around text about the world.

• Mathematical Equations are Multimodal by default

  1 project | dev.to | 21 Aug 2026

  And that stubbornness is exactly why I started building lmm, a proof of concept for everything I have been arguing in this post and in my previous one. I called it "a language agnostic framework to reality," and I meant that literally. It is a tool written in Rust that has two core commands: discover and simulate. The discover command takes raw data and attempts to find the compact mathematical equation that generated it, using the principles of symbolic regression I have been describing throughout this post. The simulate command takes a discovered equation and runs it forward in time, producing predictions that can be tested against new observations. That is the whole loop I keep talking about, observation to equation to simulation to verification. It is still a work in progress. But it exists. It runs. It discovers equations from data and simulates them forward, and that alone puts it in a fundamentally different category from any language model, because its outputs are not statistically generated text. They are mathematically derived predictions that can be checked against reality. I built it because I needed to prove to myself that the ideas I have been writing about are not just philosophy. They are engineering. They are buildable.

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  www.appsignal.com | 26 Aug 2026

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wiseaidotdev/lmm is an open source project licensed under MIT License which is an OSI approved license.

The primary programming language of lmm is Rust.