# measurement Published articles for measurement. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## \[Podcast\] 'Just say no' isn't as simple as you think DevFeed: [\[Podcast\] 'Just say no' isn't as simple as you think]() Original publisher: [Read original article]() Author: George Michaelson Published: 2026-09-16T23:23:11Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [APNIC Labs](), [Protocol (disambiguation)](), [Server]() Tags: [apnic-labs](), [dns](), [measurement](), [podcast](), [protocol](), [tech-matters]() ### AI overview A PING podcast episode continues a discussion of surplus DNS queries observed in an APNIC Labs experiment. It examines how different ways of answering negatively affect query volume and the work required by servers, resolvers, and clients, and considers whether maintaining transport protocol state could reduce repeated-query work. ### Source excerpt A continuation of the discussion on surplus DNS queries seen at APNIC Labs, the implications of how you say 'no', and the transport used for the DNS ## How sandbox boundaries affect AI coding agent evaluations DevFeed: [How sandbox boundaries affect AI coding agent evaluations]() Original publisher: [Read original article]() Author: Waldek Mastykarz Published: 2026-09-16T09:09:51Z Content type: opinion Language: en Sources: [Developer Blogs]() Topics: [AI-assisted coding](), [Testing](), [Artificial Intelligence](), [coding]() Tags: [agent-experience](), [ai](), [ai-coding](), [ax](), [coding](), [developers](), [eval](), [evaluation](), [measurement](), [sandbox]() ### AI overview The article explains that an AI coding agent evaluation can produce misleading scores when the agent retrieves answers from its prompt, environment, or other accessible resources. It argues that evaluators should define the capability being tested and set information boundaries accordingly, including restricting access to evidence when measuring internal model knowledge. ### Source excerpt Your AI coding agent passed the eval. But did the model know the answer, or did it find it somewhere on your machine? A correct answer can still invalidate your measurement. The post Your AI coding agent evaluation is only as good as its sandbox appeared first on Microsoft for Developers. ## Google tag gateway: Now on Webflow DevFeed: [Google tag gateway: Now on Webflow]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Webflow Blog]() Topics: [gateway](), [webflow](), [Google](), [Confidential Computing](), [data](), [configuration](), [Deployment]() Tags: [confidential-computing](), [configuration](), [conversion](), [data](), [deployment](), [development](), [gateway](), [google](), [measurement](), [webflow]() ### AI overview Webflow announces Google tag gateway, which routes conversion data through a site's first-party infrastructure. Existing native Google tag integration users can activate it by republishing their site, while new users can follow a code-free setup workflow. ### Source excerpt When you build on Webflow, you're creating experiences designed to help your business stand out and grow. Underpinning those experiences with a strong measurement foundation can help you understand what's driving results and make more informed marketing decisions. Now, Webflow makes it easier to strengthen your measurement foundation. ## One or two nameservers? DevFeed: [One or two nameservers?]() Original publisher: [Read original article]() Author: Geoff Huston Published: 2026-09-15T06:01:49Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Internet](), [Query (disambiguation)](), [Server](), [Caching]() Tags: [2026](), [africa](), [article](), [china](), [dns](), [europe](), [experiment](), [india](), [internet](), [ipv4](), [ipv6](), [measurement](), [measurements](), [recursive-resolver](), [repeat](), [responses](), [server](), [tech-matters]() ### AI overview This article reports an experiment testing whether serving a DNS zone with two authoritative dual-stack nameservers changes repeated queries. Compared with one nameserver, two nameservers increased the single-query completion rate from 58% to 71% and reduced the average queries per test from 3.43 to 2.57. ### Source excerpt Do multiple dual-stack nameservers increase or decrease repeat DNS queries? The results of this experiment were a complete surprise. ## How engineering teams can measure AI coding ROI with cost attribution and outcome metrics DevFeed: [How engineering teams can measure AI coding ROI with cost attribution and outcome metrics]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [ai-coding](), [Ground truth / benchmark quality](), [coding](), [Finance]() Tags: [ai](), [ai-coding](), [analysis](), [blog](), [coding](), [cost](), [datadog](), [engineering](), [governance](), [harness](), [incident](), [lifecycle](), [measurement](), [metrics](), [numbers](), [production](), [team](), [tokens](), [tooling](), [workflow]() ### AI overview A Harness panel discussion outlines ways engineering organizations can measure AI coding ROI. The panel recommends attributing costs to specific work, combining throughput metrics with qualitative evidence, and using governance and process changes to interpret results. ### Source excerpt Harness panelists share practical strategies for measuring AI coding ROI, from granular cost attribution and lifecycle metrics to budgeting, governance. | Blog ## What part of 'No!' is so hard for the DNS understand? DevFeed: [What part of 'No!' is so hard for the DNS understand?]() Original publisher: [Read original article]() Author: Geoff Huston Published: 2026-09-04T01:06:30Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [servers](), [Caching](), [DDoS](), [Cybercrime](), [Availability](), [Bot](), [Script]() Tags: [apnic-labs](), [attacks](), [availability](), [bots](), [caching](), [ddos](), [dns](), [generate](), [measurement](), [random](), [server](), [servers](), [tech-matters]() ### AI overview The article explains how random name attacks overwhelm authoritative DNS servers by generating queries for nonexistent names, bypassing recursive resolver caches and potentially causing domain availability failures. It also describes APNIC Labs' measurement work on nonexistent-domain responses to improve DNS resilience. ### Source excerpt At APNIC Labs we've been experimenting with understanding how the DNS handles requests to resolve nonexistent names to make the DNS more resilient to random name attacks. ## \[Podcast\] Measuring the impact of locally served root zone DevFeed: [\[Podcast\] Measuring the impact of locally served root zone]() Original publisher: [Read original article]() Author: George Michaelson Published: 2026-09-03T01:05:09Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [DNSSEC](), [Network](), [Security](), [Internet Engineering Task Force (IETF)](), [bug]() Tags: [bug](), [dns](), [dnssec](), [ietf](), [measurement](), [network](), [podcast](), [research](), [security](), [standards](), [tech-matters]() ### AI overview This podcast discusses research into locally served root zones, a DNS resolver model that pre-fetches and stores the root zone. The study examined BIND, Unbound, and Knot Resolver across multiple configurations, including in-band retrieval and HTTPS fetching. It identified an Unbound bug and found that root zone updates can generate substantial network traffic, potentially exceeding traffic from more frequent queries for uncached data. ### Source excerpt An analysis of the traffic impacts of locally served root zones, by Ilyas Rahimi at UvA. ## Performance Progression of Percona Server for MySQL 8.4 DevFeed: [Performance Progression of Percona Server for MySQL 8.4]() Original publisher: [Read original article]() Author: Bogdan Degtyariov Published: 2026-08-27T13:07:20Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [MySQL](), [benchmarking](), [Benchmark](), [Scalability](), [configuration](), [cpu](), [Hardware]() Tags: [2026](), [benchmark](), [benchmarking](), [benchmarks](), [concurrency](), [configuration](), [cpu](), [data](), [database](), [hardware](), [intel](), [latency](), [load](), [measurement](), [mysql](), [percona](), [percona-server-for-mysql](), [performance](), [processors](), [run]() ### AI overview This article investigates read/write performance changes across Percona Server for MySQL 8.4 versions released in 2026. It describes benchmark scope, database and buffer-size combinations, configuration, and CPU-frequency controls, while excluding features, security patches, latency percentiles, and resource-utilization measurements. ### Source excerpt 1. Purpose and scope This performance investigation aims to look into the read/write performance of Percona Server for MySQL 8.4 and how it changed between versions released in 2026: 8.4.8-8 released on 12 March 2026 8.4.10-10 released on 30 June 2026 8.4.11-11 released on 20 August 2026 We want to see if there are improvements ... Continued The post Performance Progression of Percona Server for MySQL 8.4 appeared first on Percona. ## Measuring benchmark optimization in speech recognition DevFeed: [Measuring benchmark optimization in speech recognition]() Original publisher: [Read original article]() Author: Theo Lebryk; Eric Bezzam; Alice; David Ayllon; Jakub Piotr Cłapa; Jens Madsen; Panagiotis Tzirakis Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [asr](), [Benchmark](), [benchmark overfitting machine learning](), [Machine learning](), [datasets](), [Open Source]() Tags: [asr](), [audio](), [benchmark](), [datasets](), [errors](), [measurement](), [model](), [open-source](), [research](), [speech](), [transcription]() ### AI overview The article examines benchmark optimization, or "benchmaxxing," in speech recognition. It presents three tests and evaluates 11 open-source ASR models, finding that some reproduced benchmark transcripts even when the audio contradicted them. The research also uses model ensembles and human annotations to identify and validate likely benchmark errors. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## TBM 437: Tokens, Hours, Points, and Other Curious Proxies DevFeed: [TBM 437: Tokens, Hours, Points, and Other Curious Proxies]() Original publisher: [Read original article]() Author: John Cutler Published: 2026-08-17T23:04:04Z Content type: opinion Language: en Sources: [The Beautiful Mess]() Topics: [Artificial Intelligence](), [context]() Tags: [ai](), [comparisons](), [measurement](), [metrics]() ### AI overview An opinion article examines the difficulty of measuring the return on AI token usage. It compares tokens with hours and flow metrics, arguing that measurement depends on a broader theory of value and can favor short-term, easily measured use cases. ### Source excerpt Everyone is talking about "return on tokens." Vendors love it (as long as the news is good). ## India's Internet ecosystem in 2026: Growth, resilience, and the road ahead DevFeed: [India's Internet ecosystem in 2026: Growth, resilience, and the road ahead]() Original publisher: [Read original article]() Author: Samantha Douglas Published: 2026-08-17T01:30:48Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Internet](), [Networks](), [Resilience](), [5G](), [cloud-infrastructure]() Tags: [5g](), [apnic-62](), [community](), [development](), [digital-platforms](), [digital-transformation](), [economy-report](), [education](), [finance](), [global](), [government](), [growth](), [healthcare](), [india](), [infrastructure](), [internet](), [ipv6](), [measurement](), [mobile](), [networks](), [resilience](), [scale]() ### AI overview India's Internet ecosystem has expanded through affordable mobile broadband, major digital infrastructure investment, and strong IPv6 adoption. The article examines the shift from rapid growth toward resilient, secure, and inclusive connectivity, while highlighting persistent rural and remote access gaps and the continued expansion of 4G, 5G, and fixed broadband. ### Source excerpt India has built one of the world's largest Internet ecosystems through affordable mobile broadband, digital infrastructure investment, and IPv6 leadership. As it prepares to host APNIC 62, the challenge is ensuring connectivity remains resilient, secure, and inclusive. ## Calibrating LLM-Based Population Estimates with Human Validation DevFeed: [Calibrating LLM-Based Population Estimates with Human Validation]() Original publisher: [Read original article]() Author: Hiroshi Urata Published: 2026-08-12T00:29:34Z Content type: article Language: en Sources: [Indeed]() Topics: [Large Language Model](), [human feedback](), [data](), [LLM evaluation / benchmarking]() Tags: [classification](), [data](), [data-science](), [dataset](), [evaluation](), [false-negative](), [false-positive](), [llm](), [machine-learning](), [measurement](), [validation]() ### AI overview The article explains how human validation can calibrate LLM-based population estimates. It distinguishes an LLM's apparent positive rate from true prevalence, uses human-validated samples to estimate sensitivity and specificity, and applies those error estimates to correct population-level measurements and quantify uncertainty. ### Source excerpt Key Idea Human validation is not only for evaluating an LLM. It can also calibrate how the LLM is used as a scalable measurement instrument for population estimation. An LLM can classify thousands of records at low cost, but the proportion it classifies as positive is not necessarily the true proportion in the population. By [...] ## One line of Python, and a 3x drop in p99 DevFeed: [One line of Python, and a 3x drop in p99]() Original publisher: [Read original article]() Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [SoundCloud Backstage Blog]() Topics: [Latency](), [Back end](), [bug](), [cpu](), [API](), [Python](), [Code]() Tags: [ads](), [api](), [backend](), [bug](), [code](), [cpu](), [engineering](), [latency](), [logging](), [measurement](), [monitoring](), [performance](), [python]() ### AI overview The article describes removing one log statement from a service's hot path, which substantially reduced 99th-percentile response latency and CPU usage. The service supports ad-serving decisions through an internal API, and the investigation found that validation warnings and metric increments were being emitted for expected requests from fixed-policy callers that supplied no Bayesian posterior state. ### Source excerpt We removed one log statement from a service's hot path this week. Its 99th-percentile response latency fell from a steady to , and CPU... ## Profile-guided optimization in Go DevFeed: [Profile-guided optimization in Go]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-08-09T23:17:36Z Content type: article Language: en Sources: [Daniel Lemire]() Topics: [Go Language](), [Optimization](), [Compiler](), [inlining](), [JSON]() Tags: [compiler](), [cpu](), [go](), [inlining](), [interface](), [json](), [measurement](), [optimization](), [profile](), [profiling](), [speed](), [standard-library](), [systems]() ### AI overview This article explains how profile-guided optimization in Go uses runtime measurements to guide compiler decisions such as inlining hot call sites and devirtualizing interface calls. It also describes a benchmark parsing three JSON documents with separately trained PGO builds and comparing their speed against a profile-free baseline. ### Source excerpt When a compiler optimizes your program, it has to guess. Which functions are worth inlining? Which side of a branch is the common one? Which method does this interface call actually reach? At compile time it cannot know, so it uses heuristics. Profile-guided optimization (PGO) replaces the guessing with measurement: you run your program, record ... Continue reading Profile-guided optimization in Go ## Why Two Similar Compiler Cases Cannot Share One Calling Convention DevFeed: [Why Two Similar Compiler Cases Cannot Share One Calling Convention]() Original publisher: [Read original article]() Author: Brendan Sechter Published: 2026-08-07T09:00:00Z Content type: article Language: en Sources: [Brendan A R Sechter's Development Blog]() Topics: [Compiler](), [interface](), [Back end](), [test](), [Mathematics]() Tags: [article](), [backend](), [case-study](), [class](), [compiler](), [compilers](), [engineering](), [interface](), [mathematics](), [measurement](), [verification]() ### AI overview This compiler backend case study argues that two similar cases cannot be unified when one must report two values through an interface with only one available slot. A counting argument shows that the apparent similarity of nine measured occurrences is irrelevant to the shared interface design. The article also identifies an earlier rule that unnecessarily excluded ten of twenty-four cases. ### Source excerpt A system had grown two ways of doing what looked like one thing. The obvious move was to tidy them into one. The tidying turns out to be impossible, and the reason it is impossible is the reason the two ways exist. The argument that settles it needs no specialist knowledge and fits in a sentence. One of the two cases has two things to report and only one slot to report them in. Whichever thing the slot is given, the other is lost. The other case has only one thing to report, so the single slot is exactly enough. That is a counting argument, it is decided before any code is written, and it is not the argument an engineer reaches for by default. The engineer's instinct is to look at the cases and ask whether they resemble one another. They did. Every one of the nine measured occurrences had exactly the shape that invited the tidy-up, and the measurement encouraged precisely the wrong conclusion. The resemblance was real and it was irrelevant, because the defect was never in the instances. It was in the interface they would have had to share. This article is about that distinction, which is between evidence about members of a class and evidence about the channel the class must pass through. The second dominates the first and is cheaper to check. The article reports the measurement, the way the measurement pointed the wrong direction, and the argument that settled it. It also reports a rule this author shipped one increment earlier which turns out to be stricter than the property it enforces, excluding ten of twenty-four cases for no reason. No test found that. It surfaced while gathering data for this article. How to read this The general argument is in the opening, in the section called The Argument That Settled It, and in Pattern Extraction. Those three need nothing but attention. The sections between them work the argument through a real case with real numbers, and they use the vocabulary of the trade. Every term is glossed at first use, but a reader who wants the r ## Keleusma Research Spike: Blocking Frequency as the Ordering Principle for Instruction-Set Coverage DevFeed: [Keleusma Research Spike: Blocking Frequency as the Ordering Principle for Instruction-Set Coverage]() Original publisher: [Read original article]() Author: Brendan Sechter Published: 2026-08-06T09:00:00Z Content type: article Language: en Sources: [Brendan A R Sechter's Development Blog]() Topics: [Compiler](), [Code generation](), [Code]() Tags: [article](), [code-generation](), [compiler](), [compilers](), [engineering](), [measurement](), [ordering](), [research](), [verification]() ### AI overview This case study examines how blocking frequency can guide the order of instruction implementation in Keleusma's compiler backend. It contrasts instruction-level coverage with whole-program compilability and describes a small measurement tool that exposed a flawed implementation plan. ### Source excerpt A compiler was 87 percent finished. It could not compile two thirds of the programs it was for. Both numbers are correct. The first counts individual instructions the compiler knew how to translate. The second counts whole programs that would actually go through. The gap between them is what this article is about, and the reason it exists is simple enough to state in one sentence. A program needs every instruction it uses, not most of them. One missing instruction out of a hundred stops the whole thing, exactly as one missing link stops a chain. That gap then destroyed a carefully reasoned plan. One working session before the measurement was taken, the author of this article had formally recommended what the next piece of work should be. The reasoning had no invalid step in it. The measurement showed the recommendation to be worth nothing at all, because the thing it would have unblocked does not occur even once in any program the compiler is meant to serve. The instrument that established this took about twenty minutes to build and two seconds to run. The article reports that, and then reports four errors made while writing it, all four of which ran in the direction of a more striking result, and one of which was committed inside the paragraph warning against the other three. What this is a case study of The setting is compiler engineering, and a reader who has never written a compiler can follow the argument, because the shape of the problem is not specific to compilers. The concrete project is Keleusma, whose compiler until now has emitted bytecode for a virtual machine, as described in the self-hosting strategy and its getting-started article. Native code generation is the step after that one, and it is where the ordering question first became expensive enough to measure. The lineage of the design sits in the stream-based compilers series and in the self-hosted silicon compiler. None of that background is needed to follow what follows, and the measurement stands ## TIME serves bots a different website, and User-Agent is now a billing identity DevFeed: [TIME serves bots a different website, and User-Agent is now a billing identity]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-06T01:46:28Z Content type: opinion Language: en Sources: [WorkOS Blog]() Topics: [AI Bots](), [Bot](), [Crawler](), [SIEM, Security, Observability]() Tags: [ads](), [billing](), [bots](), [curl](), [logs](), [measurement](), [openai](), [routing]() ### AI overview The article examines how TIME serves different content to crawlers based on User-Agent headers and records bot reads as billable ad impressions. It argues that because clients can freely change the header, crawler identity is an unreliable basis for billing and measurement. ### Source excerpt TIME forks its content per crawler and logs each bot read as a billable ad impression. The routing key for that whole ledger is a header any client can type. ## Metrics that matter: Measuring platform success and maturity DevFeed: [Metrics that matter: Measuring platform success and maturity]() Original publisher: [Read original article]() Author: Mallory Haigh Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [data]() Tags: [engineering](), [infrastructure](), [measurement](), [metrics](), [platform](), [platform-engineering]() ### AI overview This article argues that platform teams need structured success metrics to demonstrate value, secure funding, and guide improvement. It presents measurement across technical, adoption, and business dimensions, aligned with platform engineering maturity stages, and explains why traditional individual-output metrics fail to capture platform benefits such as reduced deployment friction, knowledge sharing, and error prevention. ### Source excerpt Frameworks to measure platform success and maturity using DORA, SPACE, and MVP metrics--linking technical outcomes to ROI, adoption, and funding ## How to measure developer productivity and platform ROI: A complete framework for platform engineers DevFeed: [How to measure developer productivity and platform ROI: A complete framework for platform engineers]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [code productivity](), [dora metrics](), [Developer experience]() Tags: [article](), [business-value](), [deployment](), [developer](), [developer-experience](), [developer-productivity](), [dora-metrics](), [efficiency](), [measurement](), [platform](), [platform-engineering](), [productivity]() ### AI overview A guide for platform engineers and technical leaders on measuring developer productivity and platform ROI. It explains why lines of code, story points, and commit counts fail to capture platform value, then introduces DORA metrics and the SPACE framework as approaches for assessing system-level improvements and developer experience. ### Source excerpt A comprehensive framework for platform engineers and leaders to measure developer productivity and platform ROI. Learn why traditional metrics fail, and how to use DORA and SPACE frameworks for accurate assessment. ## Platform engineering maturity in 2026: What the data tells us DevFeed: [Platform engineering maturity in 2026: What the data tells us]() Original publisher: [Read original article]() Author: Mallory Haigh Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [data](), [Artificial Intelligence]() Tags: [2026](), [ai](), [data](), [engineering](), [measurement](), [platform](), [platform-engineering]() ### AI overview The article uses survey results from 518 practitioners and two years of maturity data to forecast platform engineering in 2026. It reports incremental progress across the five CNCF maturity dimensions, while highlighting persistent gaps in measurement, investment, adoption, and organizational readiness. AI integration is considered critical or important by 94% of respondents, and platforms are increasingly embedding capabilities directly instead of shifting operational toil to developers. ### Source excerpt Discover 2026 platform engineering maturity trends: AI integration, product management, measurement, and investment requirements shaping the industry's future. ## The platform engineering reality check: Is your initiative on track? DevFeed: [The platform engineering reality check: Is your initiative on track?]() Original publisher: [Read original article]() Author: Mallory Haigh Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [code productivity](), [developer-productivity]() Tags: [developer](), [developers](), [engineering](), [executive](), [measurement](), [metrics](), [platform](), [platform-engineering](), [productivity](), [report]() ### AI overview This article presents a reality check for platform engineering initiatives, emphasizing measurement, return on investment, adoption, culture, and maturity. It reports that many platform teams either do not measure success or cannot demonstrate improvement, and recommends using metrics such as DORA, time to market, or SPACE. It also warns that funded teams may remain reactive without sufficient authority or strategic empowerment. ### Source excerpt Assess if your platform engineering initiative is on track. Learn about measurement, investment, adoption, culture, and maturity benchmarks for success. ## Which AI actually reads your site? Two months of LLM traffic, measured DevFeed: [Which AI actually reads your site? Two months of LLM traffic, measured]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-07-21T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [Large Language Model](), [Artificial Intelligence](), [Claude Code](), [ChatGPT](), [Google Analytics](), [Netlify](), [Back end]() Tags: [agent-experience](), [agents](), [ai](), [backend](), [chatgpt](), [claude-code](), [dx](), [google-analytics](), [jamstack](), [llm](), [llms](), [logs](), [measurement](), [netlify]() ### AI overview Evil Martians measured two months of server-side LLM traffic to its website. The study found that ChatGPT primarily fetches rendered HTML, while Claude Code requests Markdown most of the time through content negotiation. Most llms.txt requests came from bots other than the intended AI assistants, and a hidden AI hint received no measurable hits. ### Source excerpt We told you to measure. So of course we did it ourselves too: two months of server-side LLM traffic to evilmartians.com. Find out which agents read HTML, which read Markdown, what llms.txt actually does, and the one technique that got zero hits. ## Workshop Basel day two DevFeed: [Workshop Basel day two]() Original publisher: [Read original article]() Author: Daniel Stenberg Published: 2026-07-15T20:07:14Z Content type: article Language: en Sources: [Daniel Stenberg]() Topics: [HTTP](), [servers](), [Artificial Intelligence](), [Git](), [Open Source]() Tags: [ai](), [git](), [http](), [http-3](), [http-server](), [http-workshop](), [measurement](), [network](), [open-source](), [performance](), [quic](), [tests](), [web]() ### AI overview A report from the second day of a Basel workshop covering AI's influence on HTTP implementation, HTTP/2 measurements from large-scale web crawls, HTTP/1.1 behavior testing, request-smuggling tests, server performance measurement, and efforts to increase HTTP/3 and QUIC usage. ### Source excerpt If you missed it. I already described day one. Caffeinated and ready, we all gathered in the same spacious room as yesterday, but seated in new places as "suggested" by our captain. Some of us even remembered to move over the name tags we wrote yesterday to our new seats. No time was wasted on ... Continue reading Workshop Basel day two -> ## Rust-proof your code with our new Testing Handbook chapter DevFeed: [Rust-proof your code with our new Testing Handbook chapter]() Original publisher: [Read original article]() Author: "Paweł Płatek" Published: 2026-07-13T11:00:00Z Content type: tutorial Language: en Sources: [The Trail of Bits Blog](), [The Trail of Bits Blog]() Topics: [Rust](), [Security](), [mutation-testing](), [Tooling](), [Test coverage](), [Memory Safety](), [Rust formal verification](), [Concurrency](), [Claude Code](), [Claude]() Tags: [application-security](), [claude-code](), [concurrency](), [measurement](), [memory-safety](), [mutation-testing](), [rust](), [security](), [testing](), [testing-handbook](), [tooling]() ### AI overview A new Testing Handbook chapter presents techniques for security testing Rust programs and systems. It covers Rust security limitations, dynamic and static analysis, undefined-behavior detection with Miri, property testing, coverage and mutation testing, Clippy, manual code review, memory zeroization, model checking with Kani, and dependency supply-chain vetting. The article also announces rust-review, a Claude Code plugin for automated Rust security reviews. ### Source excerpt We've added a new chapter to our Testing Handbook: a comprehensive guide to security testing Rust programs. This chapter covers the tools and techniques we use at Trail of Bits to validate the security of Rust programs and systems. fn main() {(|f:&dyn Fn(u128)->Box< dyn Iterator+'static>|f(*[&( 0x7B736D70683F73u128<<64| 0x7A6A6D7C3F7A667D),&(0x7B736Du128 <<64|0x70683F7073737A77)][((std::hint:: black_box(0.0f64)/0.0).to_bits()>>63)as usize]) .for_each(|c|print!("{c}")))(Box::leak(Box::new(|n: u128|Box::new(std::iter::successors(Some(n),|&n|Some(n>>8) ).take_while(|&n|n>0).map(|n|((n as u8)^0x1F)as char))as _)))} What's in the chapter The chapter starts with a security overview of what Rust's guarantees do and don't cover, including underappreciated issues like unwind safety, nondeterminism, and arithmetic errors. This leads into an overview of dynamic analysis, which covers a range of boosters for unit tests, how to use Miri to detect undefined behavior, property testing with proptest, coverage measurement, and mutation testing. The static analysis section then covers Clippy in depth, including a list of our favorite lints. Beyond tooling, the chapter also covers what we've learned from auditing Rust codebases directly. Our gotchas and footguns checklist is a great reference for manual code reviews, and will help you find subtle issues like a & b == c having different operator precedence than in C. The memory zeroization section offers three solutions to the tricky problem of guaranteeing that secrets are erased from memory. Finally, the specialized testing sections cover tools like Kani (a model checker), and the supply chain section covers the full toolchain for vetting dependencies. Still oxidizing We've also released rust-review, a Claude Code plugin for automated Rust security reviews. Co-built with Aptos Labs, it targets over a dozen bug classes, from memory safety and concurrency hazards to FFI pitfalls and async cancellation issues. It's a fast wa [Next page]()