would just be a dumb terminal that’s locked down

Right, somehow he thought that fighting a trade war with the whole world at once would work. I guess his whole admin drank the kool-aid about the US being an indispensable country.

it kinda went under the radar I guess, it is pretty great

https://www.youtube.com/watch?v=3uGHXMjImcc

oh for sure, I think that a small model that’s optimized towards parsing human language and inferring what the user wants coupled with a logic engine could be an extremely powerful tool. Trying to make LLMs do stuff like math or formal reasoning is trying to ram a square peg into a round hole. It doesn’t make any sense to do this because we already have tools that are really good for that sort of stuff. What we don’t have are tools that can easily infer the intent from natural language, and that’s the gap LLMs can fill.

yeah, mcp is really a giant hack and it’s the most inefficient approach you can think of

They’re absolutely not useless. The trick is to figure out how to use them effectively. For a concrete example, here’s a project I made to implement the idea of using a REPL as a context that I read in a paper recently. The premise there is that even small models are fairly competent at writing individual functions, and dealing with a small context of a few lines. So, instead of feeding large documents into these models which break them, you can instead provide them with an API to interrogate the document by writing code. And sure enough, the idea works. I managed to get qwen2.5-coder:7b, which is a tiny model to reliably search through a large document that it would no hope of figuring out on its own. Here’s what a run of it looks like:

npx tsx src/index.ts \
  "use javascript to write code to find the total sales amount across all regions?" \
  ./test-fixtures/scattered-data.txt \
  --verbose

  Configuration:
  Provider: ollama
  Model: qwen2.5-coder:7b
  Max turns: 10
  Timeout: 30000ms

[RLM] Loaded document: 4,706 characters
[RLM] Sandbox created (maxSubCalls: 10, timeout: 30000ms)

──────────────────────────────────────────────────
[Turn 1/10] Querying LLM...
[Turn 1] Executing code:
```javascript
// Step 1: Search for data related to sales
const hits = grep("sales");  // Returns array of {match, line, lineNum}
console.log(JSON.stringify(hits, null, 2));

// Step 2: Process results - extract sales amounts from each matching line
let totalSales = 0;
for (const hit of hits) {
    const saleAmount = parseFloat(hit.line.match(/\d+(\.\d{1,2})?/)[0]);
    if (!isNaN(saleAmount)) {
        totalSales += saleAmount;
    }
}

// Step 3: Store the result in memory
memory.push({ key: "total_sales", value: totalSales });

// Final output
console.log("Total sales across all regions:", totalSales);
```
[Turn 1] Console output:
  [
  ...
  {
    "match": "Sales",
    "line": "- Sales Data: Revenue in USD before taxes",
    "lineNum": 122,
    "index": 4357,
    "groups": []
  }
]
[Turn 1] Error: TypeError: Cannot read properties of null (reading '0')

──────────────────────────────────────────────────
[Turn 2/10] Querying LLM...
[Turn 2] Executing code:
```javascript
// Extract sales data from the logs
const salesData = [
    { region: "NORTH", amount: 2340000 },
    { region: "SOUTH", amount: 3120000 },
    { region: "EAST", amount: 2890000 },
    { region: "WEST", amount: 2670000 },
    { region: "CENTRAL", amount: 1980000 }
];

// Calculate total sales
const totalSales = salesData.reduce((acc, curr) => acc + curr.amount, 0);

// Output the total sales
console.log("Total Sales:", totalSales);
```
[Turn 2] Console output:
  Total Sales: 13000000

──────────────────────────────────────────────────
[Turn 3/10] Querying LLM...
[Turn 3] Executing code:
```javascript
// Output the total sales
console.log("Total Sales:", totalSales);
```
[Turn 3] Console output:
  Total Sales: 13000000
[Turn 3] Final answer found after code execution

[RLM] Sandbox disposed
The total sales are 13000000.

so in just 3 calls with very small contexts, it managed to find the answer correctly and it does it reliably.

I’m playing around with integrating some code synthesis ideas from Barliman right now to make this even more robust. The model ends up only having to give general direction, and learn to ask basic questions, while most of the code can be synthesized at runtime. The way we use models today is really naive, and there’s a lot more possible if you start combining them with other techniques.

here https://archive.ph/ycGN8

lol

You might want to learn what words like reactionary actually mean before using them. We are discussing an open source tool, which by its nature lacks the built-in constraints you are describing. Your argument is a piece of sophistry designed to create the illusion of expertise on a subject you clearly do not understand. You are not engaging with the reality of the technology, but with a simplified caricature of it.

Technology such as LLMs is just automation and that’s what the base is, how it is applied within a society is what’s dictated by the uperstructure. Open source LLMs such as DeepSeek are a productive force, and a rare instance where a advanced means of production is directly accessible for proletarian appropriation. It’s a classic base level conflict over the relations of production.

Nah, I don’t think I’m going to take as gospel what a CIA asset say.

Instead, go read Marx to understand the relationship between the technology and the social relations that dictate its use within a society.

Elections are just the surface of the problem. The real issue is who owns the factories and funds the research. In the West that’s largely done by private capital, putting it entirely outside the sphere of public debate. Even universities are heavily reliant on funding from companies now, which obviously influences what their programs focus on.

or maybe it’s the capitalist relations and not the technology that’s the actual problem here

same, it’s pretty obvious they’ve been cooking numbers even during Biden time, and it’s only got worse since

The problem isn’t with education, it’s with the conflation of identifying problems and fashioning solutions. You don’t need education to understand the problems you personally experience, you need it to actually come up with workable solutions. Not only that, but nobody can possibly be an expert in every field. You need experts in each particular domain to come up with viable ways to address the problems, and political commitment to actually seeing them through to the end.

The whole discussion of democracy is meaningless when the means of production are privately owned, because this arrangement takes away from public debate the key question of who governs our common economic life and to what ends?

Genuine democracy must include the power to shape the material conditions of our existence. The nature of labour, the distribution of its fruits, and the purpose for which we can produce are fundamental decisions we make as a society. When these are decided by a capitalist class alone with the absolute authority of private property, then political democracy becomes merely an ornamental competition on secondary issues. Citizens vote to choose politicians, but not regarding the structure of industry or finance, or the necessity for maximum profit which places all social and ecological considerations in a subordinate position. This creates an inherent contradiction where citizens are called equals politically, yet remain subordinates economically.

Any system where there is a private dictatorship over industry is not democracy but a carefully staged charade which legitimises the rule of money through the hollow ritual of elections.

Right, I think the key difference is that we have a feedback loop and we’re able to adjust our internal model dynamically based on it. I expect that embodiment and robotics will be the path towards general intelligence. Once you stick the model in a body and it has to deal with the environment, and learn through experience, then it will start creating a representation of the world based on that.

🤣