Editorial
Two tech stories demand the same posture: slow down and inspect. One argues value accrues to works that reward sustained attention; the others force us to confront brittle infrastructure and a misleading metaphor that shapes how we build and regulate AI. Read fast when you must, but pay attention to what lasts — and to what our tools pretend to be.
In Brief
I like 'em thick: an apology to my English teachers
Why this matters now: The idea of "thickness" reframes how creators, educators, and platforms should judge art and learning — prioritizing work that resists instant algorithmic consumption.
The author argues that great art is built to reward time and attention: think Hieronymus Bosch’s densely packed canvases or scenes that “leave the egg out of the cake mix” so the reader must do extra work. The piece positions thick work against the algorithm-friendly thin stuff that looks good at a glance and evaporates under scrutiny.
"there is such thing as thickness"
The write-up landed because it gives a concise vocabulary for debates over AI, education, and taste — and Hacker News commenters echoed that museums and books can physically stop people in their tracks. The takeaway: if you care what endures, design for accumulation, not immediacy. Read the full essay at Experimental History.
Vomit: Clean up Claude 5's token output with a separate LLM
Why this matters now: Developers using Claude who are frustrated by messy or obfuscated assistant output can locally post-process tokens to produce clearer prose without sending telemetry to third parties.
Vomit is a pragmatic, privacy-minded tool that pipes Claude 5’s raw token stream through a local LLM to "convert…token vomit into English." It's slow and imperfect, but attractive for users who prefer local post-processing over battling model style or product settings.
"converts Claude's token vomit into English" — project pitch
Community responses sketched alternatives (persistent prompt stacks, turn-level constraints), but Vomit is notable because it sidesteps product changes and watermarking debates by rewriting after the fact. See the repo on GitHub.
CIA funding helped keep NeXT afloat in the 80s
Why this matters now: Government procurement can act like stealth venture support — buying niche tech that private markets won't, with long-term implications for what platforms survive.
Reporting shows U.S. intelligence agencies were meaningful buyers of NeXT hardware in the 1980s, helping the company scale manufacturing and survive commercial softness. This wasn't a cloak-and-dagger infiltration: agencies liked NeXT for its development environment and tooling. Hacker News readers were relieved this is procurement, not backdoor drama, but the episode is a reminder that government purchases shape which tech gets a second chance. The WSJ story has the details at WSJ.
Deep Dive
The August 17 outage
Why this matters now: GitHub's outage on August 17 demonstrates how capacity and traffic amplification can cascade through developer workflows, and the fixes they’re pushing will affect every team that depends on GitHub to ship software.
GitHub’s post-mortem pins the outage on capacity failures in a Central US data center that "failed to scale" as traffic peaked. The effect was catastrophic for developer velocity: authentication, Actions, pull requests, issues, APIs and Copilot were all impacted for nearly eight hours. GitHub puts it bluntly: "If you were trying to ship software that day, we let you down."
The recovery was operationally messy. Teams rerouted traffic, isolated infrastructure, and mitigated a client-side retry loop that amplified load — a classic retry-storm problem. Their remediation plan is aggressive: add millions of CPU cores, hundreds of petabytes of fast storage, bigger networking, a faster migration to Azure (now ~58% of load), and architectural work like linear read scaling for large monorepos.
A few lessons jump out. First, capacity isn't just raw compute — it's the ability to degrade gracefully and reject low-priority work early. Hacker News reactions emphasized traffic isolation, retry budgets, and prioritization as cheaper, faster fixes than throwing hardware at the problem. Second, client-side behaviors matter: uncontrolled retries can convert partial failures into platform-wide meltdowns. GitHub says it will standardize retry limits, tighten alerts, and isolate critical systems — changes that, if executed well, should reduce blast radius next time.
"Both incidents were capacity failures at their core" — GitHub post-mortem
For engineering teams, the outage is a reminder to design for the opaque dependencies in our pipelines and to ask vendors how they handle graceful degradation. Read GitHub's full post-mortem at GitHub News.
Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces (2025)
Why this matters now: The paper argues that treating LLM intermediate outputs (chain-of-thought) as literal reasoning traces misleads developers, policymakers, and users — and that misunderstanding has safety and governance consequences.
The position paper urges the community to avoid the habit of calling model intermediate tokens "thinking" or "reasoning traces." The authors warn this anthropomorphic shorthand "isn't a harmless metaphor, and instead is quite dangerous," because spectators may assume those tokens are windows into internal, causal steps. In reality, an intermediate token is just another token: it influences future tokens via the same forward pass mechanics, but it doesn't prove an internal "thought" or state change.
On Hacker News, defenders argued these outputs are useful as tools — they help exploration, debugging, and sometimes improve performance. Critics countered that the ELIZA effect and real-world decisions (courtrooms, audits, regulators) can be misled by plausible-but-unreliable narratives. A neat technical point someone made: printed self-corrections or "aha" moments in output don't prove an internal state was changed — they're patterns the model learned to produce when that surface representation helps meet the objective.
"we call on the community to avoid such anthropomorphization of intermediate tokens"
What should engineering teams do instead? Treat chains-of-thought as heuristics for model behavior, not transcripts of reasoning. Use them for debugging, not for trust. When stakes are high, pair models with verifiable computation (e.g., symbolic checks, external evidence retrieval, or constrained modules) rather than relying on the fluency of a produced "reason." The paper's framing matters for regulation too: laws or audits that assume intermediate outputs are interpretable could produce false assurances.
Read the authors' paper on arXiv and consider whether your team’s user stories or compliance docs lean on this appealing but risky metaphor.
Closing Thought
We’re living in a moment that prizes speed and plausible narratives. The hard work is different: making things thick enough to survive attention markets, building systems that fail gracefully under load, and resisting simple metaphors that let us pretend we understand complex models. Slow down where it counts — your users, your infra, and your metaphors will thank you.