This guide shows how to prepare a design system for reliable AI prototypes by codifying design decisions into Markdown spec files, maintaining a token layer of named variables, and running audits to catch hard-coded values. It covers using tools like FigmaLint, syncing updates, and structuring guidelines so AI always works from clear, current specs.

• Design decisions need to live in machine-readable Markdown spec files, not just visual mock-ups, so AI reads exact rules instead of guessing.
• A token layer of named variables for colors, typography, and spacing prevents AI from inventing ad-hoc values.
• Audit scripts or plugins like FigmaLint catch hard-coded values, missing states, and detached instances, feeding results back to the AI.
• A sync routine flags outdated spec files when the design system changes, keeping AI guidance current.

Boris Cherny breaks down nine common habits that burn most of your Claude tokens before the model even sees your prompt—loading CLAUDE.md, rereading chat history, forgotten hooks, and more. He shows how each pattern eats into your limits and why complaints about “Claude getting dumber” usually miss the real culprit.

• The article provides no actual list of the nine habits, specific hooks, or countermeasures beyond vague category names—despite claiming precise percentages for each.
• The claimed source (a tweet/profile labeled "Mnimiy @Mnilax") doesn't match the detailed narrative about Boris Cherny, a podcast episode, and 400 hours of usage data, suggesting fabricated or unverifiable attribution.
• The specific statistics (73% total, 14% for CLAUDE.md, 13% for chat history, 11% for hooks) are presented with false precision but no methodology or source is given for how they were measured.

This article breaks down the core concepts behind LLMs—from next-token prediction training to tokens, vectors and attention layers—to show how they generate text. It also covers context windows, parameters and why model scale affects performance.

• LLMs work purely through next-token prediction learned by hiding and guessing words billions of times during training, not through actual understanding of letters or math.
• Context windows now range wildly, from 200K tokens (~150K words) up to Llama 4 Scout's 10 million tokens, but bigger windows don't fix factual errors or logical gaps.
• Because models process text as tokens/vectors rather than raw letters, they inherently struggle with tasks like counting letters or doing arithmetic.

This article explores the overwhelming failure rate of crypto tokens, revealing that over 99.99% have effectively failed. It discusses the concentration of value in a few top tokens and the ease of creating new tokens, which contributes to the noise in the market. The author emphasizes the importance of focusing on established assets like Bitcoin and Ethereum.

• Roughly 99.99% of the ~74.5 million tokens in existence have effectively failed, with only ~500 exceeding $10M market cap.
• On Pump.fun, over 655,000 tokens were created in a single month, yet just 0.63% ever reached a decentralized exchange.
• Bitcoin alone commands ~56% of total crypto market cap, with the top ten tokens controlling nearly 90%, leaving the rest to split scraps.
• 84.7% of 2025 token generation events traded below launch valuation, with the median token down 71%.

As the new year begins, the author reflects on a more active approach to cryptocurrency trading while maintaining a lean portfolio. Highlighting the AI Agent sector and upcoming protocols like x402 and ERC-8004, they present four low-cap tokens worth researching, emphasizing the potential for gains despite the overall market's risks.

• Author is shifting to more active trading in 2026 while still keeping the overall portfolio lean/concentrated
• AI Agent sector flagged as a key theme to watch, tied to emerging protocols x402 and ERC-8004
• Four specific low-cap tokens are named as worth researching for potential upside
• Acknowledges elevated risk in the broader market despite highlighting these opportunities