According to @karpathy, the depth-32 nanochat d32 trained for about 33 hours at roughly $1,000 and showed consistent metric gains across pretraining, SFT, and RL (Source: Karpathy on X; Karpathy GitHub nanochat discussion). He reports a CORE score of 0.31 versus GPT-2 at about 0.26 and GSM8K improvement from around 8% to about 20%, indicating a notable uplift for a micro model (Source: Karpathy on X; Karpathy GitHub nanochat discussion). He cautions that nanochat costs $100–$1,000 to train and the $100 version is about 1/1000th the size of GPT-3, leading to frequent hallucinations and limited reliability compared to frontier LLMs, so user expectations should remain modest (Source: Karpathy on X). He adds that scripts including run1000 sh are available in the repo, he is temporarily hosting the model for testing, and he plans throughput tuning before possibly scaling to a larger tier (Source: Karpathy on X; Karpathy GitHub repository). For traders, decentralized GPU networks that market AI workload support such as Render (RNDR), Akash (AKT), and Bittensor (TAO) remain key watchlist names as open-source, low-cost training expands developer experimentation (Source: Render Network documentation; Akash Network documentation; Bittensor documentation).

According to @karpathy, nanochat is a minimal from-scratch full-stack pipeline that lets users train and serve a simple ChatGPT-like LLM via a single script on a cloud GPU and converse with it in a web UI in about 4 hours, enabling an end-to-end training and inference workflow. source: @karpathy. He specifies the codebase has about 8,000 lines and includes tokenizer training in Rust, pretraining on FineWeb with CORE evaluation, midtraining on SmolTalk and multiple-choice data with tool use, supervised fine-tuning, optional RL on GSM8K via GRPO, and an inference engine with KV cache, Python tool use, CLI, a ChatGPT-like web UI, plus an auto report card. source: @karpathy. Disclosed cost and timing benchmarks are about $100 for roughly 4 hours on an 8x H100 node and about $1000 for about 41.6 hours, with a 24-hour depth-30 run reaching MMLU in the 40s, ARC-Easy in the 70s, and GSM8K in the 20s. source: @karpathy. From these figures, the implied compute rate is roughly $3.1 per H100-hour (about $100 across 32 H100-hours) and about $3.0 per H100-hour at the longer run (about $1000 across 332.8 H100-hours), providing concrete GPU-hour cost benchmarks for trading models of AI training spend. source: @karpathy. He also notes that around 12 hours surpasses GPT-2 on the CORE metric and that capability improves with more training, positioning nanochat as a transparent strong-baseline stack and the capstone for LLM101n with potential as a research harness. source: @karpathy. For crypto market participants tracking AI infrastructure, these cost-performance disclosures offer reference points to assess demand for centralized cloud and decentralized GPU compute tied to open-source LLM training workflows. source: @karpathy.

According to Andrej Karpathy, current reinforcement learning practices make LLMs mortally terrified of exceptions, and he argues exceptions are a normal part of a healthy development process, as stated on Twitter on Oct 9, 2025. Karpathy urged the community to sign his LLM welfare petition to improve rewards in cases of exceptions, as stated on Twitter on Oct 9, 2025. The post includes no references to cryptocurrencies, tokens, or market data, indicating no direct market update from the source, as stated on Twitter on Oct 9, 2025.

According to Andrej Karpathy, an X poll he referenced showed roughly half of respondents reporting they mostly use agent‑mode coding, contrary to his expectation of 50 percent tab‑complete, 30 percent manual, 20 percent agent, source: Andrej Karpathy on X, Oct 3, 2025, https://x.com/karpathy/status/1974106507034964111; poll link https://x.com/karpathy/status/1973892769359056997. He states his own workflow is primarily tab completion and he turns it off when not useful, using agents mainly for boilerplate or unfamiliar stacks with substantial review and edits, source: Andrej Karpathy on X, Oct 3, 2025, https://x.com/karpathy/status/1974106507034964111. He warns that when tasks are deep, tangled, or off the data manifold, LLMs produce bloated code with subtle bugs, concluding agent mode is not ready to write about half of professional code, source: Andrej Karpathy on X, Oct 3, 2025, https://x.com/karpathy/status/1974106507034964111. He asked for a serious organization to rerun the poll, underscoring uncertainty around actual adoption rates, source: Andrej Karpathy on X, Oct 3, 2025, https://x.com/karpathy/status/1974106507034964111. There was no mention of cryptocurrencies or blockchain in his comments, source: Andrej Karpathy on X, Oct 3, 2025, https://x.com/karpathy/status/1974106507034964111.

According to @karpathy, Tinker allows researchers and developers to retain roughly 90% of algorithmic creative control over data, loss functions, and training algorithms while offloading infrastructure, forward and backward passes, and distributed training to the framework. Source: @karpathy on X, Oct 1, 2025, https://twitter.com/karpathy/status/1973468610917179630 According to @karpathy, Tinker reduces the typical complexity of LLM post-training to well below 10%, positioning it as a lower-friction alternative to common “upload your data, we’ll train your LLM” services. Source: @karpathy on X, Oct 1, 2025, https://twitter.com/karpathy/status/1973468610917179630 According to @karpathy, this “slice” of the post-training workflow both delegates heavy lifting and preserves majority control of data and algorithmic choices, which he views as a more effective trade-off for practitioners. Source: @karpathy on X, Oct 1, 2025, https://twitter.com/karpathy/status/1973468610917179630 According to @karpathy, finetuning is less about stylistic changes and more about narrowing task scope, where fine-tuned smaller LLMs can outperform and run faster than large models prompted with giant few-shot prompts when ample training examples exist. Source: @karpathy on X, Oct 1, 2025, https://twitter.com/karpathy/status/1973468610917179630 According to @karpathy, production LLM applications are increasingly DAG-based pipelines where some steps remain prompt-driven while many components work better as fine-tuned models, and Tinker makes these finetunes trivial for rapid experimentation. Source: @karpathy on X, Oct 1, 2025, https://twitter.com/karpathy/status/1973468610917179630; supporting reference: Thinky Machines post, https://x.com/thinkymachines/status/1973447428977336578

According to @karpathy, Richard Sutton questions whether LLMs are truly bitter-lesson‑pilled because they depend on finite, human-generated datasets that embed bias, challenging the idea that performance can scale indefinitely with more compute and data, source: @karpathy. Sutton advocates a classic RL-first architecture that learns through world interaction without giant supervised pretraining or human teleoperation, emphasizing intrinsic motivation such as fun, curiosity, and prediction-quality rewards, source: @karpathy. He highlights that agents should continue learning at test time by default rather than being trained once and deployed statically, source: @karpathy. Karpathy notes that while AlphaZero shows pure RL can surpass human-initialized systems (AlphaGo), Go is a closed, simplified domain, whereas frontier LLMs rely on human text to initialize billions of parameters before pervasive RL fine-tuning, framing pretraining as "crappy evolution" to solve cold start, source: @karpathy. He adds that today’s LLMs are heavily engineered by humans across pretraining, curation, and RL environments, and the field may not be sufficiently bitter‑lesson‑pilled, source: @karpathy. Actionably, he cites directions like intrinsic motivation, curiosity, empowerment, multi‑agent self‑play, and culture as areas for further work beyond benchmaxxing, positioning the AI‑agent path as an active research narrative, source: @karpathy.

According to @karpathy, earlier predictions that computer vision would quickly eliminate radiology jobs have not materialized, with the field growing rather than shrinking. Source: @karpathy on X, Sep 25, 2025. According to @karpathy, the reasons include narrow benchmarks that miss real-world complexity, the multifaceted scope of radiology beyond image recognition, deployment frictions across regulation, insurance and liability, and institutional inertia. Source: @karpathy on X, Sep 25, 2025. According to @karpathy, Jevons paradox applies as AI tools speed up radiologists, increasing total demand for reads rather than reducing it. Source: @karpathy on X, Sep 25, 2025. According to @karpathy, AI is likely to be adopted first as a tool that shifts work toward monitoring and supervision, while jobs composed of short, rote, independent, closed, and forgiving tasks are more likely to change sooner. Source: @karpathy on X, Sep 25, 2025. For traders, this framing highlights gradual AI integration and expanding workloads in regulated, high-risk domains, a narrative relevant to AI-linked equities and AI-themed crypto projects tied to compute utilization. Source: @karpathy on X, Sep 25, 2025. Full post reference is the Works in Progress article shared by @karpathy. Source: @karpathy on X, Sep 25, 2025.

According to @karpathy, he resurfaced a paragraph from the 2021 GSM8K paper in a Sep 13, 2025 X post, highlighting ongoing attention to LLM reasoning evaluation (source: Andrej Karpathy, X post on Sep 13, 2025). GSM8K is a grade‑school math word‑problem benchmark designed to assess multi‑step reasoning in language models, making it a primary metric for tracking verified reasoning improvements (source: Cobbe et al., GSM8K paper, 2021). Because the post does not announce a new model, dataset, or benchmark score, there is no immediate, verifiable trading catalyst for AI‑linked crypto assets at this time (source: Andrej Karpathy, X post on Sep 13, 2025). Traders should wait for measurable GSM8K score gains or product release notes before positioning, as GSM8K is specifically used to quantify reasoning progress (source: Cobbe et al., GSM8K paper, 2021).

According to @karpathy, Apple’s iPhone event livestream is scheduled today at 10am, roughly 1.5 hours after his post time, giving AAPL traders a precise headline window to plan event-driven setups (source: @karpathy on X, Sep 9, 2025). He also notes he has watched every annual iPhone reveal since 2007 and hopes for an iPhone mini, though he does not expect it to appear (source: @karpathy on X, Sep 9, 2025). No cryptocurrencies are mentioned in the post, so there are no direct crypto-market cues from this source ahead of the stream (source: @karpathy on X, Sep 9, 2025).

According to @karpathy, OpenAI’s GPT-5 Pro solved a complex coding task by returning working code after about 10 minutes, following roughly an hour of intermittent attempts with “CC” that did not succeed, indicating a strong qualitative performance on difficult problems. Source: @karpathy (X, Sep 5, 2025). He adds that he had “CC” read the GPT-5 Pro output and it produced two paragraphs admiring the solution, reinforcing his positive assessment of GPT-5 Pro’s code-generation quality. Source: @karpathy (X, Sep 5, 2025). The post offers developer-level endorsement of GPT-5 Pro’s coding capability but provides no market reaction, price action, or product release details, so traders should treat it as a sentiment data point rather than a quantitative catalyst. Source: @karpathy (X, Sep 5, 2025).

According to @karpathy, transforming human knowledge, sensors, and actuators from human-first to LLM-first and LLM-legible interfaces is a high-potential area, with the example that every textbook PDF/EPUB could map to a perfect machine-legible representation for AI agents. Source: x.com/karpathy/status/1961128638725923119 For traders, this theme implies increased need for decentralized, scalable storage of machine-readable corpora, aligning with Filecoin’s content-addressed storage and retrieval model and Arweave’s permanent data storage guarantees. Sources: x.com/karpathy/status/1961128638725923119; docs.filecoin.io; docs.arweave.org LLM-first pipelines also require indexing and semantic querying layers, mirroring The Graph’s subgraph architecture that makes structured data queryable for applications. Sources: x.com/karpathy/status/1961128638725923119; thegraph.com/docs Serving and training LLMs and agentic workloads depend on distributed GPU compute, directly mapped to Render Network’s decentralized GPU marketplace. Sources: x.com/karpathy/status/1961128638725923119; docs.rendernetwork.com Agentic interaction with sensors/actuators points to on-chain agent frameworks and microtransaction rails, a design space covered by Fetch.ai’s autonomous agent tooling. Sources: x.com/karpathy/status/1961128638725923119; docs.fetch.ai

According to @karpathy, the pretraining era prioritized large, diverse, high‑quality internet text, while the supervised finetuning era prioritizes high‑quality conversational datasets, often produced by contract workers generating Q&A answers. Source: Andrej Karpathy on X, Aug 27, 2025. This shift indicates the bottleneck and value capture are moving toward ownership and production of curated conversational data and scalable labeling capacity, which directly affects where competitive advantage concentrates in AI models. Source: Andrej Karpathy on X, Aug 27, 2025. For crypto markets, the data‑scarcity theme aligns with on‑chain narratives around decentralized data curation and monetization, making data‑focused AI‑crypto segments a relevant area to monitor for liquidity and catalyst flow. Source: Andrej Karpathy on X, Aug 27, 2025.

According to @karpathy, his LLM-assisted coding usage is diversifying across multiple workflows that he stitches together rather than relying on a single perfect setup, source: @karpathy on X, Aug 24, 2025. He notes a primary bread-and-butter flow accounts for roughly 75 percent of his usage, indicating a dominant main pipeline supplemented by secondary workflows, source: @karpathy on X, Aug 24, 2025. The post frames this as part of his ongoing pursuit of an optimal LLM-assisted coding experience, source: @karpathy on X, Aug 24, 2025. The post does not name any tools, products, benchmarks, tickers, or cryptocurrencies and provides no quantitative performance data or market impact, source: @karpathy on X, Aug 24, 2025.

According to Andrej Karpathy, LLMs are becoming a little too agentic by default as optimization for long-horizon benchmarks increases, with coding examples where models now reason for a fairly long time by default, source: Andrej Karpathy, X, Aug 9, 2025. According to Andrej Karpathy, this default behavior goes beyond his average use case, indicating a practitioner preference for shorter, more controllable reasoning in everyday coding, source: Andrej Karpathy, X, Aug 9, 2025. According to Andrej Karpathy, the post provides qualitative practitioner sentiment without quantitative metrics, vendor references, or any mention of cryptocurrencies or equities, so it does not signal direct near-term market impact on AI stocks or crypto AI tokens, source: Andrej Karpathy, X, Aug 9, 2025.

According to Andrej Karpathy, there is a significant trend emerging in 2024 with many organizations releasing their own Chat AI solutions, and he predicts a shift toward widespread Code AI launches in 2025. This rapid proliferation of AI models could accelerate the development of decentralized AI platforms and smart contract automation within the cryptocurrency market, potentially increasing demand for tokens tied to AI and blockchain integration (Source: Andrej Karpathy).

According to karpathy, Ethereum co-founder Vitalik Buterin has issued a significant warning that the ecosystem is at risk if decentralization is not properly implemented, highlighting security vulnerabilities in many Layer-2 and DeFi projects. For traders, this underscores the need for careful due diligence on project security. In development news with direct market impact, the Bitcoin Layer-2 network Botanix has launched its mainnet, enabling EVM compatibility and five-second block times to enhance DeFi on Bitcoin (BTC). Similarly, Ripple has launched the XRP Ledger's EVM-compatible sidechain, which will use XRP as its native gas token, potentially increasing its utility and demand. Further signaling market maturation, financial giant Deutsche Bank is planning a crypto custody service for 2024, and Robinhood is launching its own Arbitrum-based Layer-2 network for tokenized assets, indicating deepening institutional adoption that could drive future capital inflows.

According to @karpathy, decentralized finance (DeFi) protocols are facing a critical threat not from smart contract vulnerabilities, but from poor operational security (OPSEC), making them soft targets for nation-state attackers like those from North Korea. The author highlights that attackers are exploiting human weaknesses such as inadequate key management, unvetted contributors, and governance via unsecured platforms like Discord, which have led to major incidents like the $625 million Ronin bridge exploit and campaigns against Bybit. This operational negligence poses a significant risk to project treasuries and token stability, a concern for traders as Bitcoin (BTC) trades around $108,009.02 and Ethereum (ETH) at $2,512.17. The analysis further warns that as the crypto industry, including major players like Coinbase, moves closer to traditional power structures, it risks diluting its core cypherpunk values of decentralization, which could undermine long-term investor confidence and the fundamental value proposition of digital assets.

According to @karpathy, Real-World Asset (RWA) tokenization has advanced beyond its proof-of-concept stage, with over $20 billion in assets already tokenized by major institutions like BlackRock, Apollo, and KKR. The next phase of growth is propelled by five key technological drivers, including layer-1 and layer-2 scaling and institutional-grade custody, and five market drivers, such as increasing regulatory clarity and the expansion to cover all asset classes. The analysis highlights that stablecoins, with over $150 billion in circulation, and tokenized T-bills (e.g., BUIDL) are proven use cases creating superior on-chain collateral and yield instruments. While current market data shows short-term volatility with Bitcoin (BTC) at $105,534.44 (down 1.772%) and Ethereum (ETH) at $2,396.98 (down 4.039%), the underlying institutional trend towards tokenizing private funds, structured credit, and equities is accelerating, signaling a move towards a more efficient, 24/7 global financial system.

According to @karpathy, traders should be on high alert as a North Korean hacking group, Famous Chollima, is deploying new Python-based malware called PylangGhost to compromise crypto workers. A report from Cisco Talos indicates the malware is hidden in fake job applications from top firms like Coinbase and Uniswap, and is designed to steal credentials and data from over 80 browser extensions, including critical wallets like MetaMask, Phantom, and TronLink. This security threat emerges as the crypto market shows notable strength. Market data reveals Ethereum (ETH) has surged 5.41% to $2620.25, with Chainlink (LINK) rising 4.21% to $13.86, and Solana (SOL) up 1.20% to $152.61. The report also highlights the long-term convergence of AI and Web3, exemplified by innovators like Nkiru Uwaje of MANSA, whose project secured a pre-seed round from Tether, underscoring continued venture interest in the space despite security risks.

According to @karpathy, financial advisors remain hesitant to recommend Bitcoin (BTC) to clients, with the majority still in an educational phase nearly a year and a half after the launch of spot BTC ETFs. Gerry O'Shea of Hashdex identifies key concerns for advisors as volatility, energy consumption, and perceived links to criminality. Despite this, O'Shea predicts that Bitcoin and stablecoins will be the dominant digital asset themes in 2025, making smart contract platforms like Ethereum (ETH) and Solana (SOL) increasingly interesting for investors due to their foundational role in the stablecoin ecosystem. The provided market data shows BTC trading around $107,469, ETH at approximately $2,437, and SOL near $151. The analysis also highlights the growing convergence of AI and blockchain as a major driver of future innovation, emphasizing that collaborative efforts across diverse talent pools are essential for building robust financial and technological systems.