Robert Youssef Profile picture
Feb 12 11 tweets 5 min read Read on X
SemiAnalysis just published data showing 4% of all public GitHub commits are now authored by Claude Code.

their projection: 20%+ by year-end 2026.

in the same week, Goldman Sachs revealed it embedded Anthropic engineers for 6 months to build autonomous accounting agents.

a thread on the week ai stopped being a tool and started being a coworker:Image
let's start with the Goldman story because it's the one that should make every back-office professional pause.

Goldman's CIO told CNBC they were "surprised" at how capable Claude was beyond coding. accounting, compliance, client onboarding, KYC, AML.

his exact framing: "digital co-workers for professions that are scaled, complex, and very process intensive."

not chatbots answering FAQs. autonomous agents parsing trade records, applying regulatory rules, routing approvals.

they started with an ai coding tool called Devin. then realized Claude's reasoning engine works the same way on rules-based financial tasks as it does on code.

the quiet part: Goldman's CEO already announced plans to constrain headcount growth during the shift. no mass layoffs yet. but "slower headcount growth" is how corporations say "we're replacing the next hire, not the current one."Image
now the SemiAnalysis numbers.

4% of GitHub public commits. Claude Code. right now. not projected. not theoretical. measured.

the tool has been live for roughly a year. it went from research preview to mass platform impact faster than almost any dev tool in history.

and that 20% projection isn't hype math. SemiAnalysis tracks autonomous task horizons doubling every 4-7 months. each doubling unlocks more complex work: snippet completion at 30 minutes, module refactoring at 4.8 hours, full audits at multi-day horizons.

the implication isn't "developers are getting faster." it's that the definition of "developer" is expanding to include anyone who can describe a problem clearly.Image
the model race itself has turned into something i've never seen before.

on February 5, Anthropic and OpenAI released new flagship models on the same day. Claude Opus 4.6 and GPT-5.3-Codex. simultaneously.

Opus 4.6 took #1 on the Vals Index with 71.71% average accuracy and #1 on the Artificial Analysis Intelligence Index. SOTA on FinanceAgent, ProofBench, TaxEval, SWE-Bench.

GPT-5.3-Codex fired back with top scores on SWE-Bench Pro and TerminalBench 2.0, plus a claimed 2.09x token efficiency improvement.

this isn't annual model releases anymore. it's weekly leapfrogging. the gap between "best model" and "second best" now lasts days, not months.Image
but the real signal isn't the models. it's who's building the infrastructure around them.

Apple shipped Xcode 26.3 with native agentic coding support. Claude Agent and OpenAI Codex now work directly inside Xcode. one click to add. swap between agents mid-project.

Apple redesigned its developer documentation to be readable by ai agents.

read that again. Apple is designing docs for ai to read, not just humans.

the company that spent decades perfecting human-facing interfaces is now optimizing for machine-facing ones.
OpenAI launched "Frontier," an enterprise platform for managing ai agents the way companies manage employees.

onboarding processes. performance feedback loops. review cycles.

HP, Oracle, State Farm, Uber already signed on.

Accenture is training 30,000 professionals on Claude. the largest enterprise deployment so far, targeting financial services, life sciences, healthcare, and public sector.

the language has shifted. nobody at these companies is saying "ai assistant" anymore. they're saying "digital workforce."Image
meanwhile, the unverified but plausible claims from this week's briefing paint an even wilder picture:

reportedly, racks of Mac Minis in China are hosting ai agents as "24/7 employees." ElevenLabs is pushing voice-enabled agents that make phone calls autonomously. OpenAI is supposedly requiring all employees to code via agents by March 31, banning direct use of editors and terminals.

i can't confirm all of these yet. but the verified stuff alone, Goldman embedding ai accountants, 4% of GitHub already automated, Apple redesigning docs for machines, tells you the trajectory is real even if some individual claims aren't.
the financial infrastructure is reacting in real time.

memory chip prices reportedly surged 80-90% in Q1. global chip sales projected to hit $1 trillion this year.

the compute demand from agentic ai isn't theoretical. it's already straining supply chains.

and with terrestrial resistance to data center construction growing (New York lawmakers reportedly introduced a moratorium bill), the pressure is building for creative solutions. orbital compute. alternative energy. distributed processing.

the physical world is scrambling to keep up with the virtual one.
the broader pattern from this week:

ai stopped being a product category and became an employment category.

Goldman doesn't want a "Claude product." it wants Claude employees.

Apple doesn't want ai features. it wants ai-native development.

OpenAI isn't selling an api. it's selling Frontier, a platform to manage your agent headcount.

the abstraction layer between "tool" and "worker" collapsed in a single week.
and because no week in 2026 is complete without the absurd:

bonobos were reportedly found to identify pretend objects, further proving symbolic thought isn't unique to humans.

and in China, a blackout was allegedly caused by a farmer trying to transport a pig via drone across mountainous terrain. the pig hit power lines.

we've been saying the singularity will arrive "when pigs fly."

apparently it just did.
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More from @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:Image
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?Image
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
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: Image
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.Image
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
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 ↓ Image
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. Image
Read 9 tweets
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) 👇 Image
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
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: Image
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.Image
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."Image
Read 17 tweets
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.Image
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.Image
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. Image
Image
Read 8 tweets

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