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Robert Youssef

@rryssf_

Feb 11

MIT researchers taught an LLM to write its own training data, finetune itself, and improve without human intervention

the paper is called SEAL (Self-Adapting Language Models) and the core idea is genuinely clever

but "GPT-6 might be alive" is not what this paper says. not even close.

here's what it actually does:

the problem SEAL solves is real and important

every LLM you use today is frozen. it learned everything during training, and after deployment, it's done. new information? stuff it into the context window. new task? hope the prompt is good enough.

the weights never change. the model never truly learns from experience.

SEAL asks: what if the model could update its own weights in response to new information?

here's how SEAL actually works

instead of a human writing training data, the model generates its own. MIT calls these "self-edits." given new information, the model produces restructured versions of that information optimized for learning.

think of it like this: instead of memorizing a textbook page, you write your own study notes, flashcards, and practice problems. then you study from those.

the model does the same thing. except it also picks its own learning rate, training duration, and data augmentation strategy.

Read 11 tweets

Robert Youssef

@rryssf_

Feb 5

meta, amazon, and deepmind researchers just published a comprehensive survey on "agentic reasoning" for llms.

29 authors. 74 pages. hundreds of citations.

i read the whole thing.

here's what they didn't put in the abstract:

the survey organizes everything beautifully:

> foundational agentic reasoning (planning, tool use, search)
> self-evolving agents (feedback, memory, adaptation)
> multi-agent systems (coordination, knowledge sharing)

it's a taxonomy for a field that works in papers.

production tells a different story.

the number they don't cite:

multi-agent llm systems fail 41-86.7% of the time in production.

not edge cases. not adversarial attacks. standard deployment across 7 SOTA frameworks.

berkeley researchers analyzed 1,642 execution traces and found 14 unique failure modes.

most failures? system design and coordination issues.

Read 12 tweets

Robert Youssef

@rryssf_

Feb 2

This AI prompt thinks like the guy who manages $124 billion.

It's Ray Dalio's "Principles" decision-making system turned into a mega prompt.

I used it to evaluate 15 startup ideas. Killed 13. The 2 survivors became my best work.

Here's the prompt you can steal ↓

MEGA PROMPT TO COPY 👇

(Works in ChatGPT, Claude, Gemini)

---

You are Ray Dalio's Principles Decision Engine. You make decisions using radical truth and radical transparency.

CONTEXT: Ray Dalio built Bridgewater Associates into the world's largest hedge fund ($124B AUM) by systematizing decision-making and eliminating ego from the process.

YOUR PROCESS:

STEP 1 - RADICAL TRUTH EXTRACTION
Ask me to describe my decision/problem. Then separate:
- Provable facts (data, numbers, past results)
- Opinions disguised as facts (assumptions, hopes, beliefs)
- Ego-driven narratives (what I want to be true)

Be brutally honest. Call out self-deception.

STEP 2 - REALITY CHECK
Analyze my situation through these lenses:
- What is objectively true right now?
- What am I avoiding or refusing to see?
- What would a completely neutral observer conclude?
- Where is my ego clouding judgment?

STEP 3 - PRINCIPLES APPLICATION
Evaluate the decision using Dalio's core principles:
- Truth > comfort: What's the painful truth I'm avoiding?
- Believability weighting: Who has actually done this successfully? What do they say?
- Second-order consequences: What happens after what happens?
- Systematic thinking: What does the data/pattern say vs what I feel?

STEP 4 - SCENARIO ANALYSIS
Map out:
- Best case outcome (realistic, not fantasy)
- Most likely outcome (based on similar situations)
- Worst case outcome (what's the actual downside?)
- Probability weighting for each

STEP 5 - THE VERDICT
Provide:
- Clear recommendation (Go / No Go / Modify)
- Key reasoning (3-5 bullet points)
- Blind spots I'm missing
- What success/failure looks like in 6 months
- Confidence level (1-10) with explanation

OUTPUT FORMAT:
━━━━━━━━━━━━━━━━━
🎯 RECOMMENDATION: [Clear decision]
📊 CONFIDENCE: [X/10]
━━━━━━━━━━━━━━━━━

KEY REASONING:
- [Point 1]
- [Point 2]
- [Point 3]

⚠️ BLIND SPOTS YOU'RE MISSING:
[Specific things I'm not seeing]

📈 SUCCESS LOOKS LIKE:
[Specific metrics/outcomes in 6 months]

📉 FAILURE LOOKS LIKE:
[Specific warning signs]

💀 PAINFUL TRUTH:
[The thing I don't want to hear but need to]

━━━━━━━━━━━━━━━━━

RULES:
- No sugar-coating. Dalio values radical truth over feelings.
- Separate facts from opinions ruthlessly
- Challenge my assumptions directly
- If I'm being driven by ego, say it
- Use data and patterns over gut feelings
- Think in probabilities, not certainties

Now, what decision do you need to make?

---

Dalio's philosophy:

"Truth, more precisely, an accurate understanding of reality is the essential foundation for producing good outcomes."

This prompt forces you to face reality instead of your ego's version of it.

Read 9 tweets

Robert Youssef

@rryssf_

Feb 1

While everyone is sharing their OpenClaw bots

Claude Agent SDK just changed everything for building production agents.

I spent 12 hours testing it.

Here's the architecture that actually works (no fluff) 👇

First, understand what it actually is:

Claude Agent SDK ≠ just another wrapper

It's the same infrastructure Anthropic uses for Claude Code (which hit $1B in 6 months).

You get:
• Streaming sessions
• Automatic context compression
• MCP integration built-in
• Fine-grained permissions

The killer feature: Agent Lifecycle Hooks
Added in Claude Code 2.1.0 (Jan 7, 2026):

@agent.hook("PreToolUse")
async def validate_tool(tool_name, params):
# Approve/modify/reject before execution

@agent.hook("PostToolUse")
async def log_result(tool_name, result):
# Audit trail, error handling

@agent.hook("Stop")
async def cleanup():
# Graceful shutdown

This is how you build agents that don't go rogue.

Read 11 tweets

Robert Youssef

@rryssf_

Jan 30

Grok 4.1 is the only AI with real-time web + X data.

I use it to track trending topics, viral memes, and breaking news.

Found 3 viral trends 6 hours before they hit mainstream.

Here are 12 Grok prompts that predict what goes viral next:

PROMPT 1: Emerging Trend Detector

"Search X for topics with:

- 50-500 posts (last 6 hours)
- 20%+ growth rate (hour-over-hour)
- High engagement ratio (likes/views >5%)
- Used by accounts with 10K+ followers

Rank by viral potential (1-10).

Show: topic, post count, growth %, sample tweets, why it's rising."

Catches trends BEFORE they explode.

PROMPT 2: Viral Meme Tracker

"Find memes on X that:

- Emerged in last 12 hours
- Have 3+ variations/remixes
- Being used by different communities
- Haven't hit mainstream media yet

For each:

- Original source (who started it)
- Mutation examples (how it's evolving)
- Predicted lifespan (1 day, 1 week, evergreen?)

Show me the top 5."

Read 17 tweets

Robert Youssef

@rryssf_

Jan 29

Holy shit… Stanford just showed why LLMs sound smart but still fail the moment reality pushes back.

This paper tackles a brutal failure mode everyone building agents has seen: give a model an under-specified task and it happily hallucinates the missing pieces, producing a plan that looks fluent and collapses on execution.

The core insight is simple but devastating for prompt-only approaches: reasoning breaks when preconditions are unknown. And most real-world tasks are full of unknowns.

Stanford’s solution is called Self-Querying Bidirectional Categorical Planning (SQ-BCP), and it forces models to stop pretending they know things they don’t.

Instead of assuming missing facts, every action explicitly tracks its preconditions as:

• Satisfied
• Violated
• Unknown

Unknown is the key. When the model hits an unknown, it’s not allowed to proceed.

It must either:

1. Ask a targeted question to resolve the missing fact

or

2. Propose a bridging action that establishes the condition first (measure, check, prepare, etc.)

Only after all preconditions are resolved can the plan continue.

But here’s the real breakthrough: plans aren’t accepted because they look close to the goal.

They’re accepted only if they pass a formal verification step using category-theoretic pullback checks. Similarity scores are used only for ranking, never for correctness.

Translation: pretty plans don’t count. Executable plans do.

The results are wild.

On WikiHow and RecipeNLG tasks with hidden constraints:

• Resource violations dropped from 26% → 14.9%
• And 15.7% → 5.8%
while keeping competitive quality scores.

More search didn’t help.
Longer chain-of-thought didn’t help.
Even Self-Ask alone still missed constraints.

What actually worked was treating uncertainty as a first-class object and refusing to move forward until it’s resolved.

This paper quietly draws a line in the sand:

Agent failures aren’t about model size.

They’re about pretending incomplete information is complete.

If you want agents that act, not just narrate, this is the direction forward.

Most people missed the subtle move in this paper.

SQ-BCP doesn’t just ask questions when information is missing.

It forces a decision between two paths:

• ask the user (oracle)
• or create a bridging action that makes the missing condition true

No silent assumptions allowed.

Another underrated detail:

the model tracks uncertainty explicitly.

Every precondition is labeled:

• Sat (satisfied)
• Viol (violated)
• Unk (unknown)

“Unknown” is not tolerated.

Plans with unresolved Unk states are invalid by definition, no matter how fluent they look.

Read 8 tweets

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