DIGNALEGI

AI Engineering

A personal, scored reading index

A focused reading list

Build AI systems around uncertainty, not around demos.

These essays focus on the engineering work that begins after a prototype: evaluation, reliability, observability, tool use, workflow design, and operating probabilistic systems in production.

AI Engineering / From the index

Parsing “Can” from “Should”: How Netflix's Product Team Got Thousands of Creatives to Trust an AI Overhaul

First Round Review September 2026

Why read thisAI adoption should start with desired experiences, broken workflows, and explicit human tradeoffs, not model capabilities.

Summary available

Open reading room
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is sampled with gaps, but the visible portions are substantive enough to support a strong judgment.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
A human eye meets an optical lens apparatus.
02

Make no assumptions.

Why read thisAI-assisted work can create hidden layers of bad reasoning that look polished enough to become load-bearing.

Summary available

Will Larson — Irrational Exuberance July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence appears substantive, but the judgment is limited to the supplied extracted text and cannot assess any omitted sections or presentation quality.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
03

How does programming language affect token efficiency and correctness?

Why read thisShorter code need not mean cheaper AI programming: task difficulty, recurring mistakes and evaluation design can overwhelm apparent advantages from language concision.

Some background helpful · Summary available

Dan Luu August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled with gaps, so the full structure and all empirical details are not visible.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
04

LLM Security Basics: The Full Threat Model

Why read thisLLM risk starts with the missing boundary between instructions and data, then concentrates when agents combine untrusted text, private access, and outward action.

Some background helpful · Summary available

ByteByteGo August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence appears complete, but references themselves are not included in the supplied evidence.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →

AI Engineering archive

Filter by score
70+

48 pieces · Highest scores first.

05

AI enthusiasts are in a race against time, AI skeptics are in a race against entropy

Why read thisAI enthusiasts and skeptics may both be reacting to real threats, with gains and downstream costs visible to different groups.

Some background helpful · Summary available

Charity Majors June 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is sampled with gaps, so completeness and all supporting examples cannot be fully judged.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
06

How to name things

Why read thisNames are compressed explanations of system meaning, and choosing them can expose hidden ambiguities in data models and behavior.

Some background helpful · Summary available

kolemannix.com August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is substantial and argument-dense; its value comes from experience and conceptual analysis rather than research support.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
07

Agentic test processes, LLM benchmarks, and other notes on agentic coding from Galapagos Island

Why read thisLuu argues coding agents become leverageable when strong tests, feedback loops, and variance-aware evaluation constrain their failures.

Some background helpful · Summary available

Dan Luu July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled, but it includes extensive first-hand mechanisms and examples supporting the judgment.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
08

Torn Down to Last

Why read thisPreserving working knowledge requires supervised practice that transfers judgment, alongside decisions about which inherited systems deserve to survive.

Summary available

Mike Fisher via mikefisher.substack.com · Fish Food for Thought October 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The supplied text substantively supports the argument and recommendations; historical details and external code-change statistics have not been independently verified.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
09

TBM 437: Tokens, Hours, Points, and Other Curious Proxies

Why read thisPrecise input meters can make ROI look rigorous while hiding the untested theory of value that actually determines return.

Summary available

The Beautiful Mess — John Cutler August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled with gaps, so omitted sections may affect balance or completeness.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
10

From the AI Engineering index

Algorithms Trap Us in the Familiar. Can They Also Spark Breakthroughs?

Why read thisEfficiency-tuned algorithms can erase experts’ creative advantage by sending independent thinkers into the same narrow solution space.

Summary available

sloanreview.mit.edu via MIT Sloan Management Review August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The public text reports study results, but the full methods and statistical details are not independently visible in the supplied evidence.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
11

How to evaluate LLMs before production

Why read thisProduction-bound LLM evaluation works when it tests a product decision under realistic ambiguity, with outcomes and guardrails kept distinct.

Some background helpful · Summary available

GitHub Blog August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is substantive; specific reported results were not independently verified and are judged only as presented.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
12

Your alt text passes automated checks. That doesn’t mean it’s any good.

Why read thisAccessibility automation works better when provable string errors and context-dependent judgments about image meaning are treated as different problems.

Summary available

GitHub Blog August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is a product-engineering post and cannot establish broad real-world effectiveness beyond the described implementation and tests.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
13

Bug blindness

Why read thisExperienced insiders can stop seeing brokenness because small learned workarounds make a flawed product feel normal.

Summary available

Dan Luu August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled with gaps, so the score is based on visible argument quality and examples, not a complete assessment of the full article.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
14

AI demands more engineering discipline. Not less

Why read thisCheap AI-generated code raises the value of specifications, tests, observability, production feedback, and repeatable validation.

Some background helpful · Summary available

Charity Majors June 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled with gaps, so some connective argument and caveats may be missing.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
15

Adapting to AI: Leadership

Why read thisBreck argues AI stratifies leadership: routine management gets cheaper, while systems-minded leaders matter more when AI can amplify dysfunction.

Some background helpful · Summary available

blog.colinbreck.com via Colin Breck July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled with gaps, so the full essay's continuity and balance cannot be completely verified.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
16

The broken windows theory of coding agents

Why read thisCoding agents can compress codebase decay from years into days by replicating weak patterns before review habits catch up.

Some background helpful · Summary available

Manager.dev September 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The article is grounded mainly in one team's experience and self-reported workflow numbers, not an independent cross-company study.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
17

Exercises in benchmarking and evals, part 7: DeepSWE, Senior SWE-Bench, napkin math, and winter tires

Why read thisBenchmark numbers are least portable when hidden setup choices, workload mismatch, and scoring discontinuities determine what they appear to measure.

Some background helpful · Summary available

Dan Luu July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled, but the provided sections substantively demonstrate the article's method and claims.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
18

Roadmap decisions rather than dates.

Why read thisRoadmap pressure can be a decision-throughput problem, where prototypes, fewer handoffs, and clearer tradeoffs shrink ambiguity.

Summary available

Will Larson — Irrational Exuberance August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is substantive, but some AI-tooling claims are forward-looking and not fully evidenced in the excerpt.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
19

How to Fight Clickbait: Meta, LinkedIn & YouTube Case Studies

Why read thisClickbait weakens when feed retrieval shifts from cheap engagement proxies toward meaning, though each architecture pays a different operational price.

Some background helpful · Summary available

ByteByteGo August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The article says it relies on publicly shared sources; source quality and diagrams are not independently verifiable from the supplied evidence.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
20

Your contributors are AI-first now. Is your project?

Why read thisAgent-written contributions become manageable when maintainer judgment is encoded as local repo rules, hard gates, and deliberate boundaries.

Some background helpful · Summary available

GitHub Blog August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence appears substantive and complete enough for judgment, though the final promotional GitHub community section may be less valuable.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
21

How accurate have Ed Zitron's AI skeptic predictions been?

Why read thisEd Zitron’s AI-skeptic forecasts are audited as failures of both accuracy and reasoning: selective metrics, shifting claims, and numerical decoration.

Summary available

Dan Luu September 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Historical score; not verified under the current evidence-review process.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
22

AI Evals: A Hands-On Guide for Product Teams

Why read thisAI evals measure acceptable output in probabilistic workflows, using context-specific correctness, recurring error patterns, and baselines.

Some background helpful · Summary available

Product Talk — Teresa Torres September 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is sampled with gaps, so completeness and all examples cannot be fully judged.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
23

GraphRAG: How AI Answers Questions Hidden Across Many Documents

Why read thisGraphRAG targets questions whose answers emerge from corpus-wide relationships and patterns, not wording similarity to one retrievable passage.

Some background helpful · Summary available

ByteByteGo August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled with gaps, and external references or benchmark claims were not independently checked.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
24

Slop-Creep: When Building Gets Cheaper Than Thinking

Why read thisAI can make building cheaper than judgment, producing slop-creep: systems and features created before anyone asks whether they should exist.

Summary available

awaitinginput.substack.com
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence contains a strong argument and one study reference; academic support is limited, and some observations rely on personal or organisational experience.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
25

There's no reason for software to be slow anymore

Why read thisLLM agents lower the cost of workload-specific performance work, making niche optimizations newly worth trying.

Brief pending · original available

Dan Luu August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled, informal, and self-described as quick/non-rigorous, so claims should be treated as exploratory.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
26

The right kind of AI sceptic

Why read thisAI scepticism and optimism both fail when they become identities protected from firsthand use, specific claims, and contrary evidence.

Summary available

The Engineering Manager June 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is argument-driven and anecdotal; it does not establish broader empirical prevalence of the described patterns.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
27

Does Code Quality Still Matter?

Why read thisTechnical debt may persist after human-readable code if machine-facing structures become costly for LLMs to change.

Brief pending · original available

blog.ploeh.dk via Ploeh Blog July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The article is a speculative thought experiment, not an evidence-backed empirical analysis.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
28

Human judgment doesn't leave the software factory. It relocates.

Why read thisAI coding moves human judgment toward intent, architecture, risk, verification, trust boundaries, and final ownership rather than removing it.

Some background helpful · Summary available

Addy Osmani August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Sampled evidence is long but incomplete; some implementation details and examples cannot be fully checked.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
29

Own the Outer Loop

Why read thisAgentic engineering makes human judgment the scarce layer: constraints, evidence standards, release decisions, and ownership when autonomous work fails.

Some background helpful · Summary available

Addy Osmani July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is long and substantive, but some cited studies and reports are not verifiable from the supplied text alone.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
30

Audit your Agent files

Why read thisAgent instructions are perishable configuration: useful for hard-to-infer workflow constraints, harmful when they accumulate stale generic prose.

Some background helpful · Summary available

Addy Osmani August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is substantive, but some cited studies are summarized rather than fully shown, so research quality cannot be independently assessed from the excerpt.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
31

How we make AI coding more cost efficient without sacrificing task quality

Why read thisGitHub argues coding-agent efficiency depends on whole-task context economics, not shaving tokens from isolated model or tool interactions.

Some background helpful · Summary available

GitHub Blog September 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Judgment is based only on the supplied extracted article text; the final HydraFusion sentence appears contextually unrelated and was not used as evidence.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
32

How we engineer feedback at Figma with eng crits

Why read thisEarly, non-approving engineering critique turns technical design from gatekeeping into structured exploration before teams overcommit.

Summary available

figma.com via Editor’s archive · Pocket
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence provides a detailed internal process, but generalizability beyond Figma's culture and tooling is not established.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
33

Extreme Programming 1999->2026

Why read thisChanging practices should honor stable values—including whether AI gains benefit workers—in an argument drawing on Kent Beck’s Extreme Programming.

Some background helpful · Summary available

manager.dev via Manager.dev August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is sampled with gaps, so the full structure and possible repetition cannot be fully assessed.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
34

Not just development, distribution of software may change as well

Why read thisAI coding may turn repositories into adaptable examples, experimental branches, and agent-readable rails, not just polished release artifacts.

Summary available

antirez July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The argument is forward-looking and partly speculative; evidence supports the author's reasoning, not that the predicted shift will occur.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
35

How Supabase became the essential infrastructure for the AI era | Paul Copplestone (Co-founder, CEO)

Why read thisPaul Copplestone frames Supabase’s growth as disciplined sequencing: win developers early, rely on Postgres and open standards, then broaden operations.

Some background helpful · Summary available

First Round Review June 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Historical score; not verified under the current evidence-review process.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
36

IC work is the new career flex

Why read thisVerna argues that AI-assisted average competence can make leadership-scale work executable by one context-rich individual instead of a coordinated team.

Summary available

elenaverna.com
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The argument relies largely on personal experience and current AI work practices; data support is limited.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
37

Software Factories, Light and Dark

Why read thisAI software factories become dangerous when generation scales faster than human comprehension and machine-verifiable checks.

Some background helpful · Summary available

Addy Osmani July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Historical score; not verified under the current evidence-review process.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
38

AI 'aha' team meetings

Why read thisAI adoption spreads through situated, emotionally safe vicarious learning, not generic workflow posts or isolated trial and error.

Summary available

Lara Hogan March 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence appears complete, but judgment is limited to the supplied article text.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
39

Don’t Outsource Your Thinking

Why read thisCode agents amplify judgment when treated as context-sensitive executors, not substitutes for domain reasoning, testing, or engineering discipline.

Some background helpful · Summary available

teltam.github.io via Editor’s archive · Raindrop January 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is a source-page extraction and supports the main argument, but it cannot prove the article's full editorial polish beyond the supplied text.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
40

State of AI | OpenRouter

Why read thisReal AI usage looks plural, workload-specific, and increasingly agent-like, not like a single winning model absorbing every task.

Some background helpful · Summary available

openrouter.ai via Editor’s archive · Raindrop December 2025
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled and the study covers one platform with proxy measures, limiting generalization.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
41

How Gamma pulled off their AI pivot | Jon Noronha (Co-founder and CPO of Gamma)

Why read thisGamma's pivot worked when generative AI solved the blank-page problem, collapsing onboarding, product value, virality, and willingness to pay.

Summary available

First Round Review July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled interview text, so the full episode’s depth and repetition cannot be fully assessed.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
42

In defense of AI mandates

Why read thisFunded AI mandates buy time, slack, enablement, and managerial cover; unfunded ones just add unpaid work and ambiguity.

Summary available

Charity Majors July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence supports the management argument, but does not show case data or outcomes from a specific mandate.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
43

When non-devs open PRs

Why read thisAI-assisted PRs from non-engineers create operational mess around review ownership, code boundaries, and who is accountable for changes.

Brief pending · original available

manager.dev via Manager.dev August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Some quantitative claims come from a sponsor-linked report and are not independently validated here.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
44

Your Team's AI Is Siloed. Here's How to Start Fixing It.

Why read thisSachin Rekhi argues team AI gains fail to compound when prompts, refinements, and judgment remain trapped inside private sessions.

Summary available

Reforge August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The piece is a recap with promotional links, though the provided evidence contains enough substantive guidance to assess.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
45

Harnesses are Situated Agents

Why read thisCoding harnesses may be better understood as situated agents: infrastructure that shapes context, execution, memory, teams, and persistence.

Some background helpful · Summary available

dbreunig.com via Dan Breunig August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence appears to be the source page, but the discussion includes many current product examples that may date quickly.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
46

The "I don't know, Claude wrote this" pandemic

Why read thisAI-assisted code becomes cognitive surrender when engineers ship PRs they cannot explain or defend.

Brief pending · original available

manager.dev via Manager.dev June 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Referenced external article and book claims are not evaluated beyond the supplied excerpts.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
47

The death and revival of the hands-on Engineering Manager

Why read thisHands-on engineering management returns as AI changes the work too fast for leaders to judge from second-hand reports.

Brief pending · original available

manager.dev via Manager.dev August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Assessment is limited to the provided text and does not verify the broader industry claim.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
48

More than just code review

Why read thisEffective coding-agent use, the argument goes, depends on precise instruction and verification beyond line-by-line review.

Brief pending · original available

Simon Willison August 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Historical score; not verified under the current evidence-review process.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →