That’s the beauty of having a cult though.

I feel like this sort of stuff is an intentional insult to the Europeans, like he’s not even trying to maintain any sort of decorum. And the key part is that he knows he doesn’t have to, no matter what he demands, or how crudely, the Europeans will eventually oblige.

I see what you did there

I think kurds served as a wedge when Syria was relatively united under a secular government. Now that it’s run by a jihadists, there’s no more need for them because the whole country is now splitting up across sectarian lines. Investing in kurds is just not worth the resources anymore. This was actually a pretty insightful interview on the whole thing https://www.youtube.com/watch?v=sqiBRc3NXMw

At this point, it should be clear to anybody paying even minimal attention that all the professed liberal values merely exist to justify western imperialism. And that’s what makes liberals far more deplorable than conservatives in my opinion. Liberals will always do mental gymnastics to rationalize invasions and destruction of other countries pretending that it’s really for their own good.

Weird how liberals stopped talking about human rights abuses in Syria as soon as they installed a pro western regime there.

good point

The propagandists have their work cut out for them with this one. And thanks!

I wonder if Europeans will ever learn that debasing themselves in front of Trump only invites more punishment.

What’s even funnier is that meta literally spent millions on each one of them.

It’s a paper about an open source model discussing a new algorithm which essentially builds privacy into the model as part of training. Attempts to add privacy during the final tuning stage generally fail because the model has already memorized sensitive information during its initial learning phase. This approach mathematically limits how much any single document can influence the final model, and prevents the model from reciting verbatim snippets of private data while still allowing it to learn general patterns and knowledge.

I don’t think there’s any chance of Netherlands, or any other European country, going against the US in any meaningful way.

People aren’t keen on dying for a western backed fascist regime. Who knew.

It’s really spurring Chinese companies to make LLMs that don’t need a lake of water to tell you how many r’s there are in strawberry. 🤣

I think slop should really be defined by the purpose of the art rather than the medium. Any piece of advertisement is inherently far more slop than a piece of genAI art somebody made because they just had an idea in their had they wanted to express.

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.

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.