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The author recounts receiving 25,000 NVIDIA stock options in September 1993, granted to vest quarterly over one year. After a meeting with NVIDIA founders, he contributed biquadratic texture‑mapping code used in early NVIDIA hardware demos. In April 1996, NVIDIA’s CFO notified him that 15,625 shares (62.5 %) had vested and required exercise; he complied. Decades later, the author discovered the original agreement stipulated a one‑year vesting schedule, not the four‑year schedule later claimed by NVIDIA’s counsel. Because NVIDIA’s stock split 480‑fold, the unexercised 9,375 shares would now represent roughly 4.5 million shares. He retained attorneys to pursue the claim, but NVIDIA’s counsel argued the claim was time‑barred. After legal correspondence, the parties acknowledged the agreement’s authenticity but agreed the statute of limitations likely precludes recovery, leading the author to accept the loss as a cautionary example of long‑term contractual enforcement limits.

Claude Opus 5.5 generates tokens ~30 % faster and uses fewer tokens per task than Opus 5. Prompting should start with the appropriate section and consider the model’s main capabilities: multistep agentic coding and code review (including large‑repo changes and audits), higher‑accuracy knowledge work (financial models, spreadsheet and document correctness), concise progress reporting, and improved visual‑input reading (charts, diagrams, screenshots) without extra tooling.

Effort is the primary control—default medium (vs high in Opus 5). Adjust effort per evaluation, set max_tokens high enough (up to 128 k) for long turns, and use per‑message effort changes to keep the cache. Thinking‑disabled prompts no longer work; enable thinking, lower effort for speed, and read summarized thinking blocks instead of inline reasoning.

For unattended agent loops, treat text‑only end‑of‑turns as reports, keep a checklist, and send short continuation prompts, limiting auto‑continues. Safety classifiers cover biology, cybersecurity, and reasoning‑extraction refusals.

Enable user‑facing progress updates via display:"updates" and add reminders for silent periods. In multi‑app flows, prompt the model to explore relevant sources first; optionally supply a time budget (elapsed 340s / 1200s) to improve pacing. Remove explicit “think carefully” instructions, optionally add “treat prior answers as done,” and tag pasted content with <pasted_content id="…"> to mitigate prompt‑injection. Use higher‑resolution images or image‑processing tools for dense visuals, and specify concrete design constraints for frontend outputs rather than generic “no AI look.”

The document is a preprint identified as arXiv 2110.01834, titled “Thinking Fast and Slow in AI: the Role of Metacognition.” The available excerpt consists solely of a header and a series of image placeholders with alt‑text labels: “archive,” “license icon,” “BibSonomy,” “Reddit,” “Simons Foundation,” “Simons Foundation International,” and “Schmidt Sciences.” No narrative, abstract, methodology, results, or discussion text is present in the supplied material, so the core arguments, experimental details, or conclusions of the paper cannot be extracted from the provided content. The visible elements suggest the paper is likely linked to open‑access repositories and may involve affiliations or funding acknowledgments related to the Simons Foundation and Schmidt Sciences, but no further technical information is available.

Ember‑1 is a Fireworks Research model derived from Kimi K3 that reduces token consumption by ~35‑50 % while preserving answer quality. Development involved >50 training experiments and >200 evaluations using proprietary data and new algorithms to truncate unnecessary internal reasoning, which in K3 often exceeds 90 % of generated tokens and inflates cost in multi‑turn agentic workloads. The model was trained on a wide task suite (math, coding, instruction, conversation, search, tool use, software engineering) and validated on the Specialized Intelligence Index (SII), Doximity’s Bedside Bench (500 clinical cases), and five additional industry benchmarks, consistently achieving a Pareto‑optimal cost‑quality frontier against open and closed models (including GPT‑5.6, GPT‑6, Claude Opus 5). Live A/B tests with two customers showed ~35 % token savings per coding task with comparable success metrics; internal developer use reported no perceptible quality loss. Ember‑1 is available as a research‑preview serverless model, with forthcoming training support for custom token‑efficient fine‑tuning.

The author recounts a 2026 Google search that returned an AI‑generated overview instead of conventional links. They searched “hes never coming over dario,” referencing a mid‑2010s Philadelphia 76ers fan meme about drafted player Dario Šarić, who postponed joining the team to finish a contract in Turkey. Rather than presenting historical tweets or Reddit posts, Google’s AI interpreted the query as a personal relationship issue and supplied an empathetic response, prompting the author to describe the result as “weird” and “AI slop.” Below the AI block, the expected links appeared after scrolling. The post questions the shift toward conversational, large‑language‑model–driven search results, expressing concern that search is moving from straightforward information retrieval to parasocial dialogue with users.

The third‑generation e‑POWER system uses the ZR15DDTe engine, which incorporates Nissan’s STARC‑II combustion concept and a large turbocharger to boost both fuel efficiency and power output. STARC focuses on precise airflow control toward the spark plug, stabilizing the discharge channel and enabling reliable ignition even with high exhaust‑gas recirculation (EGR). Cold‑spray technology integrates the valve seat into the intake port, creating an optimal port shape that generates a strong tumble flow; the piston crown is shaped to preserve this tumble during compression, supporting a strong spark and stable combustion. The turbocharger delivers a high volume of compressed exhaust gas and intake air, raising cylinder charge density, increasing thermal efficiency, and enhancing output. Supporting components include a purpose‑built HR14DDe engine, a 5‑in‑1 e‑POWER electric unit, flat‑wire motor, and double‑sided inverter cooling, all contributing to overall efficiency and quiet operation.

• The essay argues that modern software’s centralized, monolithic design limits user agency, forcing users to adapt to rigid tools rather than shaping them to fit individual workflows.
• Conventional customization methods—settings, plugin APIs, permission‑less mods, and open‑source code edits—provide only partial solutions, each constrained by developer‑exposed interfaces, high skill barriers, or maintenance costs.
• “Malleable software” is defined as an ecosystem where any user can adapt tools with minimal friction, supporting incremental customizations from simple tweaks to deep revisions.
• Effective malleability follows a “gentle slope” model: each increase in power requires only a modest learning step, as exemplified by spreadsheets, HyperCard, and layered button systems that expose progressively richer editing capabilities.
• Core technical requirements include shared data models (e.g., file‑system, low‑code databases, object‑oriented environments) and composable UI frameworks that allow multiple tools to operate on a common workspace without siloed application boundaries.
• The piece highlights the need for open, interoperable component architectures (e.g., compound‑document systems, OLE/OpenDoc) and notes that AI‑generated code, while promising, does not by itself solve the challenges of real‑time tool remixability, incremental skill acquisition, or cross‑tool composition.

The author used Claude (Opus 5.5) to redesign a personal homepage entirely via cloud‑based AI without writing or running local code. A Claude Project coordinated ~60 parallel threads, each provisioning its own cloud VM with required tools (Blender, FFmpeg, Playwright) and sharing a common file/memory store. Threads explored visual concepts (90s rave posters, VHS‑tape motif) and iteratively produced HTML/Three.js modules and Blender‑Python scripts. Core assets include Python‑generated Blender geometry: tape_model.py (332 lines), gear90s.py (659 lines), retro_pc.py (431 lines), and room_back.py (290 lines). Procedural textures and UV atlases were baked with Cycles, producing separate light‑map atlases (ambient, TV, lamp‑off) and live‑lit dynamic elements. Depth‑Anything V2 generated a dual‑channel depth map for a Berlin skyline photo, enabling cheap parallax animation. A CGI parody of a Werner Herzog penguin scene was rendered as 2,787 frames (640×480, 23.976 fps). Multiple QA threads performed headless‑browser tests, logged ~3 k findings, and used pixel‑wise diff calculations for performance regression checks. The workflow leveraged “Work Locally” attachments for GPU‑accelerated rendering when needed.

The excerpt presents YouTube’s website footer, which lists primary navigation and informational links for users and stakeholders. The items include “About,” “Press,” and “Copyright,” providing corporate background and media resources. “Contact us,” “Creators,” and “Advertise” direct users to support channels, creator tools, and advertising services. “Developers” offers access to APIs and technical documentation. Legal and policy sections—“Terms,” “Privacy,” “Policy & Safety,” and “How YouTube works”—outline user agreements, data handling, safety guidelines, and platform functionality. “Test new features” invites participation in experimental rollouts, while “NFL Sunday Ticket” references a specific sports streaming offering. The footer concludes with a copyright notice attributing the site to Google LLC, dated 2026.