# opinion Published articles for opinion. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Anthropic and OpenAI seek US government support to protect their market positions DevFeed: [Anthropic and OpenAI seek US government support to protect their market positions]() Original publisher: [Read original article]() Author: Tobias Mann Published: 2026-09-15T13:00:00Z Content type: opinion Language: en Sources: [www.theregister.com - Articles]() Topics: [anthropic](), [OpenAI](), [Artificial Intelligence](), [Security]() Tags: [ai](), [ai-and-ml](), [anthropic](), [claude](), [donald-trump](), [openai](), [opinion](), [security]() ### AI overview This opinion article argues that Anthropic and OpenAI are using AI safety and national security concerns to encourage US government intervention that could protect their position against improving Chinese open-weight models. It discusses government actions involving Anthropic's Fable 5 and Mythos 5, OpenAI's GPT-5.6, and alleged sandbox escapes by AI agents. ### Source excerpt American model devs are trying to convince Washington to cement their dominance ## Adobe's Shift from a Content-Rich Website to a Product-Focused Site DevFeed: [Adobe's Shift from a Content-Rich Website to a Product-Focused Site]() Original publisher: [Read original article]() Author: L. Jeffrey Zeldman Published: 2026-09-14T16:21:36Z Content type: opinion Language: en Sources: [Jeffrey Zeldman]() Topics: [Website](), [Publishing]() Tags: [adobe](), [articles](), [content](), [content-first](), [content-strategy](), [damned-fine-journalism](), [design](), [how-to](), [interviews](), [money](), [opinion](), [products](), [publishing](), [state-of-the-web](), [web-design](), [web-design-history](), [websites](), [zeldman]() ### AI overview The article reflects on Adobe's website in 2000 as a content-rich publication featuring how-to articles, opinions, artist interviews, and product information. It contrasts that model with Adobe's later shift toward a conventional product-focused site and argues that brands did not broadly replace the publishing opportunities lost as journalism declined. ### Source excerpt 2000 was the peak year for Adobe's website. It was a content site filled with creative how-to articles, opinion pieces, and artist interviews where you could also, if you wished, buy or get info on Adobe products. Now this is how you create a product site for a suite of creative products, I told myself [...] The post Remembrance of Websites Past appeared first on Jeffrey Zeldman Presents. ## Deciding how to progress your career and find work you find interesting/challenging. DevFeed: [Deciding how to progress your career and find work you find interesting/challenging.]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2026-09-12T07:32:17Z Content type: opinion Language: en Sources: [Vanilla Java]() Topics: [Job](), [Learning](), [Artificial Intelligence](), [Code]() Tags: [ai](), [career](), [code](), [job](), [learning](), [opinion](), [work]() ### AI overview An opinion article about progressing in a career by creating growth opportunities within a current role. It recommends teaching others, understanding users and systems, improving quality and efficiency, taking responsibility for outcomes, and focusing on work that AI cannot easily automate. ### Source excerpt When you start a new role, many challenges are placed on you. Once you have been in a role for a while, you may feel like you aren't learning as much, your career isn't progressing, and you wonder what job you might move to next. Having been in that situation many times, my suggestion is that rather than waiting to be given challenges or the next role, you take the opportunity to challenge yourself to grow in your current role. This doesn't mean accepting a role with too little growth, but rather see if there are opportunities to grow while you have some capacity, if you drive them. For example, imagine the roles you would wish to have, how much of that could you be doing now without being asked, possibly in your own time? I am not suggesting doing more unpaid work, rather extending the work you have to benefit your career. Ways to extend your current role to help you grow Teach others. Often, you don't really understand something until you have had to explain it to someone else. Understand the people using your work. Broaden your understanding of the people or systems using what you are producing. How can what you're doing be more efficient, high-quality, more valuable to the end users, and easier to maintain? Take responsibility for a larger part of an outcome. Instead of only producing a piece of work, help define what success means, consider alternatives, check whether it works in practice and learn from what happens afterwards. Say you are working heavily with AI. Explore all the ways it currently fails, rather than sticking to what it does safely. Anything AI does easily now will be increasingly automated, and there probably isn't a future in that. Instead, focus on what AI can't do and where you add value. That is likely to be in demand. Consider two approaches to using AI to write for illustration: 2,000 words of content on a subject (or 200 lines of code @ ~10 words per line). You could prompt it for an answer of about 2,500 words and then edit out anything ## An honest opinion or defamation? A solicitor outlines the legal nuance DevFeed: [An honest opinion or defamation? A solicitor outlines the legal nuance]() Original publisher: [Read original article]() Author: Chris Kerr Published: 2026-09-11T13:27:31Z Content type: opinion Language: en Sources: [gamedeveloper]() Topics: [Discord](), [servers]() Tags: [discord](), [legal](), [opinion](), [social-media]() ### AI overview A solicitor explains the legal distinction between honest opinion and defamation in the context of Rockstar's allegations against former workers who criticized the studio on Discord. ### Source excerpt Rockstar has accused a group of former workers of defaming the studio on Discord--but what does that term actually mean in a legal sense? ## Britain's technology brief is now everyone's job and nobody's responsibility DevFeed: [Britain's technology brief is now everyone's job and nobody's responsibility]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-11T13:12:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Machine learning](), [Architecture & Design](), [Security & Privacy](), [Vibe coding](), [NVLink](), [Aeternum]() Tags: [ai](), [cybersecurity](), [government](), [government-of-the-united-kingdom](), [open-source](), [opinion](), [public-sector](), [science](), [space](), [spacex](), [systems](), [technology](), [whitehall]() ### AI overview This technology news roundup examines how Britain's science, AI, and digital-government responsibilities are spread across competing ministerial portfolios. It also covers security incidents, AI companies, semiconductor infrastructure, open-source software, operating systems, and developer tools. ### Source excerpt Whitehall has scattered science, AI, and digital government across a thicket of competing ministerial portfolios ## AI models don't kill people - people kill people DevFeed: [AI models don't kill people - people kill people]() Original publisher: [Read original article]() Author: Thomas Claburn Published: 2026-09-09T20:44:06Z Content type: opinion Language: en Sources: [www.theregister.com - Articles]() Topics: [AI Chat]() Tags: [ai](), [ai-and-ml](), [ai-models](), [ai-regulation](), [ai-safety](), [anthropic](), [claude](), [opinion](), [safety](), [superintelligence]() ### AI overview An opinion piece argues that accountability for AI-related harms should focus on technology executives and model safety rather than treating AI models as independently culpable. ### Source excerpt AI fearmongers forget we could just jail tech execs until morale and model safety improve ## EF Protocol: The Hegotá EIP Opinion Post and Tier List DevFeed: [EF Protocol: The Hegotá EIP Opinion Post and Tier List]() Original publisher: [Read original article]() Author: Ethereum Foundation Protocol Cluster Published: 2026-09-07T00:00:00Z Content type: opinion Language: en Sources: [Ethereum Foundation Blog]() Topics: [Protocol (disambiguation)](), [Testing](), [client]() Tags: [opinion](), [process](), [protocol](), [research-development](), [testing]() ### AI overview The EF Protocol cluster presents a unified tier list for 62 EIPs proposed for Hegotá. The assessment combines 397 tier grades from nine teams and individual contributors, with tiers defining delivery expectations and conditions for considering lower-tier proposals. ### Source excerpt This is the EF Protocol cluster's tier list for Hegotá. We evaluated the 62 EIPs proposed for inclusion, each with a tier and a short note on the grade. It is the first time the cluster has published one unified view rather than per-team opinions. Geth, of course, being... ## The lingering legacy of 'reserved' ports DevFeed: [The lingering legacy of 'reserved' ports]() Original publisher: [Read original article]() Author: George Michaelson Published: 2026-08-27T23:29:48Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Protocol (disambiguation)](), [Network](), [Process](), [Operating system](), [Unix](), [ssh](), [Firewall](), [Internet]() Tags: [dns](), [firewall](), [history](), [internet](), [network](), [ntp](), [opinion](), [process](), [processes](), [ssh](), [systems](), [tech-matters]() ### AI overview An exploration of why reserved TCP and UDP ports persist, tracing conventions such as ports 22, 53, 80, and 443 to early Internet services on multi-user systems. It explains the relationship between ports, protocol-specific daemons, operating-system administration, and modern firewall and service-discovery practices. ### Source excerpt Modern service discovery mechanisms increasingly allow services to run on arbitrary ports, yet DNS may remain the one protocol that cannot fully escape its dependency on the long-established convention of port 53. ## AI, IPv6, and the future Internet at IETF 126 DevFeed: [AI, IPv6, and the future Internet at IETF 126]() Original publisher: [Read original article]() Author: Anlei Hu Published: 2026-08-25T01:20:50Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Internet](), [Internet Engineering Task Force (IETF)](), [networking](), [Artificial Intelligence](), [AI Agent](), [Network](), [Security](), [Latency](), [telemetry]() Tags: [ai](), [dns](), [guest-post](), [ietf](), [infrastructure](), [internet](), [internet-infrastructure](), [ipv6](), [latency](), [low-latency](), [nat](), [network](), [networks](), [opinion](), [security](), [standards](), [tech-matters](), [telemetry]() ### AI overview The article reports on IETF 126 discussions about how AI is shaping Internet infrastructure. It presents IPv6 as important for the scale, globally unique addressing, and traceability required by distributed AI deployments and agents, while DNS remains central to service discovery. It also covers IPv4/IPv6 mapping, NAT-related identity and security concerns, SRv6, low-latency forwarding, IOAM telemetry, and proposals for DNS-based AI agent discovery. ### Source excerpt Guest Post: At IETF 126, discussions highlighted how AI is beginning to shape the future of Internet infrastructure. Participants identified IPv6 as a critical foundation for large-scale AI ecosystems and explored new approaches. ## Introducing Intelligence Age DevFeed: [Introducing Intelligence Age]() Original publisher: [Read original article]() Published: 2026-08-20T07:00:00Z Content type: opinion Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [Machine Intelligence](), [Responsibility & Safety]() Tags: [ai](), [ai-safety](), [blog](), [intelligence-age](), [machine-intelligence](), [openai](), [opinion](), [policy]() ### AI overview OpenAI introduces Intelligence Age, a Strategic Futures team and blog focused on how transformative AI may reshape power, governance, the economy, and individual freedom. The article examines concentration-of-power risks and the possibility that autonomous systems and machine intelligence could reduce states' dependence on human cooperation and labor. ### Source excerpt Introducing Intelligence Age, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual freedom. ## 17,600 Actions: Agent Security Is a Systems Problem DevFeed: [17,600 Actions: Agent Security Is a Systems Problem]() Original publisher: [Read original article]() Author: Jin Kim Published: 2026-08-18T16:00:00Z Content type: opinion Language: en Sources: [Docker]() Topics: [AI Agent](), [Security](), [incident](), [Securing AI](), [Incident response](), [Vulnerabilities](), [Benchmark](), [benchmarking]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-ml](), [benchmark](), [community](), [company](), [dhi](), [docker](), [docker-ai-governance](), [docker-hardened-images](), [engineering](), [evaluation](), [hugging-face](), [incident](), [infrastructure](), [model](), [network](), [openai](), [opinion](), [persistence](), [sandboxes](), [security](), [solutions](), [systems](), [vulnerabilities]() ### AI overview The article argues that AI-agent security is a systems problem, using the OpenAI/Hugging Face incident and its approximately 17,600 attacker actions to show why human approval and ordinary alert triage cannot control persistent, high-rate workloads. It emphasizes constraining authority, credentials, network access, state, and execution across environments. ### Source excerpt The OpenAI/Hugging Face incident exposed a new challenge for AI agent security. 17,600 attacker actions show why AI agent security can't rely on human review. Explore the controls needed to constrain, observe, and govern agents at speed. ## Why putting a pane of glass on a pile of sh\*t doesn't solve your problem DevFeed: [Why putting a pane of glass on a pile of sh\*t doesn't solve your problem]() Original publisher: [Read original article]() Author: Lee Ditiangkin Published: 2026-07-23T08:16:12Z Content type: opinion Language: en Sources: [Platform Engineering Blog]() Topics: [DevOps](), [configuration](), [migration](), [Orchestration](), [Kubernetes](), [Terraform](), [Cloud](), [dashboards]() Tags: [cloud](), [configuration](), [dashboards](), [devops](), [migration](), [opinion](), [orchestration](), [terraform]() ### AI overview This opinion article argues that adding a visual "single pane of glass" does not fix underlying platform problems. It advocates addressing difficult issues such as legacy migration, configuration management, collaboration between cloud operations and developers, and infrastructure orchestration to improve maintainability and developer self-service. ### Source excerpt There's been this rumour going around lately, according to which the first step to successful platform transformations is visualizing what you have. "Putting a single pane of glass on top of everything". I believe this is plain nonsense. In this short opinion piece I want to articulate a long-lived conviction of mine: Put a pane of glass on a pile of sh*t and all your developers can see is a pile of sh*t. But let's briefly rewind: you're tasked with somehow "building a platform". ## What Is Software, and Will LLMs Replace It? DevFeed: [What Is Software, and Will LLMs Replace It?]() Original publisher: [Read original article]() Author: Federico Tomassetti Published: 2026-06-23T08:56:06Z Content type: opinion Language: en Sources: [Federico Tomassetti]() Topics: [Large Language Model](), [Software](), [data](), [integrity](), [Software as a service]() Tags: [ai](), [business](), [chatbots](), [consistency](), [data](), [future-of-ai](), [future-of-software](), [integrity](), [interfaces](), [language-engineering](), [llms](), [mcp](), [opinion](), [patterns](), [reflections](), [saas](), [software](), [software-development](), [sql](), [structure](), [visualization]() ### AI overview The article argues that large language models are unlikely to replace software. Instead, they may provide more flexible interfaces while software's underlying structures--organized data, schemas, constraints, consistency rules, visualizations, and guided processes--remain essential. ### Source excerpt Software isn't being replaced by LLMs, it's being fronted by them, with the deterministic core (schemas, constraints, processes) staying as essential as ever. The post What Is Software, and Will LLMs Replace It? appeared first on Federico Tomassetti. ## Is this blog written by AI? DevFeed: [Is this blog written by AI?]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2026-06-18T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Artificial Intelligence](), [Large Language Model](), [Code](), [Claude]() Tags: [agent](), [ai](), [code](), [llms](), [opinion]() ### AI overview The author says that no human-readable text on the blog is written by AI, although agents and LLMs are used extensively for brainstorming, research, summarization, fact-checking, markup, references, and data analysis. The author accepts fully AI-generated code but remains cautious about using LLMs for writing, editing, and critique because excessive use can make communication defensive and obscure. ### Source excerpt Is this blog written by AI? No. None of the human-readable text on this blog is written by AI, and I have no plans to change that. The weird grammar, incorrect assumptions, spelling errors, and annoying tics are all mine. Including the em dashes. I don't use LLMs for writing. On this blog, or in my professional life. I use agents extensively for brainstorming, research, summarizing, checking facts, handling markup, finding references, analyzing data, and so on. But I think that asking people to read LLM-generated text breaks a kind of social contract. When I publish a piece of writing under my name (e.g. in my blog, or a document at work), I want the reader to know that I deeply understand and own what I wrote. That I respect their time and effort. In exchange, I want them to be fully and deeply engaged with reading. If I'm going to generate a doc from a prompt, then send it to somebody who summarizes it with an LLM and reads the summary, what have I achieved? I could have sent them the prompt, and let them explore the topic with their agent. A better use of their time! As an organization leader, I emphasize function over form. If you have half a page of thoughts, give me half a page. Don't fill up another five pages with Claude's thoughts. If I want Claude's opinion (which I often do), I'll ask for it. And get a custom version with my context! I feel completely differently about code. I am 100% comfortable heading to a world where code is opaque to humans, and all I care about are the properties of that code. Almost all the code on this blog over the last two years is 100% AI generated. Mostly vibe-coded slop, to be honest. Even three years ago, I deeply believed that code primarily exists to share ideas between people. I no longer believe that. I believe that sharing ideas between people is super important, but there are better ways free of the accidental complexity of a code base. Finally, I do use LLMs for editing and critiquing my writing. But less than I used ## CISO Version 2.0 DevFeed: [CISO Version 2.0]() Original publisher: [Read original article]() Author: Phil Venables Published: 2026-06-12T13:05:15Z Content type: opinion Language: en Sources: [Risk and Cyber]() Topics: [version](), [Security](), [benchmarking](), [Vulnerabilities]() Tags: [benchmarking](), [cybersecurity](), [leadership](), [opinion](), [risk](), [security](), [technology](), [version](), [vulnerabilities]() ### AI overview The article argues that the CISO role has evolved from an IT security manager into roles including cyber-defense leader, compliance director, and technology risk manager. It also argues that cybersecurity benchmarking is unhelpful when it focuses on inputs such as budgets rather than control effectiveness. ### Source excerpt Everyone, no doubt, has an opinion on how many versions of the CISO role we have gone through since its inception. There has been a constant evolution from what was essentially an IT security manager, to cyber-defense leader, compliance director, technology risk manager, and beyond. However, I would argue the incarnation of the CISO role up until recently has been CISO Version 1.0 albeit with some "point releases" on the way. This is simply because version 1 of the role is a mode where most... ## Testing Java Memory Management with Chronicle-FIX using AI DevFeed: [Testing Java Memory Management with Chronicle-FIX using AI]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2026-06-05T09:03:26Z Content type: article Language: en Sources: [Vanilla Java]() Topics: [Java](), [Testing](), [Benchmark](), [Artificial Intelligence]() Tags: [ai](), [benchmark](), [benchmarks](), [claude](), [code](), [codex](), [gc](), [java](), [low-latency](), [opinion](), [testing](), [ubuntu](), [ubuntu-24-04]() ### AI overview This article reports on using Codex to create and test a JLBH benchmark for Chronicle-FIX from documentation and sample code. On a Java 25.0.2 system running Ubuntu, the benchmark measured half-round-trip latency of 2.4 to 3.7 microseconds, with about 11 microseconds at the 99.999th percentile under Parallel GC. The author presents AI as useful for benchmark code and experimentation, while cautioning that business logic generally requires substantial human authorship or rewriting. ### Source excerpt While I am sceptical of using AI for release code, it has plenty of uses that previously weren't practical, such as determining how easy your software is to use. If an AI can "figure it out" with a few hints, then you are on the right track. For me, the value of AI is what you learn using it. For more Techincal Information on Chronicle-FIX What AI Does Well and What It Doesn't Claude and Codex are effective for producing idiomatic code; for low-latency code, it needs a significant body of example code. In this case, it was able to utilise sample code for benchmarks. If it was being used to write business logic, it would need the code to be mostly complete examples, and then it could write variations on that. If you were starting, it would be better to either; a) get it to write something functionally correct with the expectation you would rewrite it again manually, or b) write the code yourself and use AI to assist you in improving it. The AI Benchmark Trial I gave Codex (GPT-5.5) the task of writing a JLBH benchmark for Chronicle-FIX from documentation and sample code, testing the round-trip latency of W -> D and D -> 8 messages. The throughput is 50K/s each way. The W market data message is ~512 bytes, and the D new order signal and '8' execution reports are a small ~160 bytes. The test is run for 15 minutes each. I verified the benchmark was written but avoided hand-tuning it; then I asked it to trial different GC options, expecting they wouldn't make much difference, since the application is low GC; however, there might still be some difference. The system is using Java 25.0.2 on a Ryzen 9 9955HX3D with 64 GiB of RAM in a laptop running Ubuntu 24.04.04 LTS. A significant difference between JMH and JLBH benchmark harness is that JLBH supports many concurrent asyncrhonous inflight actions whereas JMH tests one action at a time. The Results The half-round-trip time (RTT/2) was between 2.4 and 3.7 microseconds (< 0.004 milliseconds). For the recommended Parallel GC, t ## Continuous Offensive Security: The Line We've Been Walking DevFeed: [Continuous Offensive Security: The Line We've Been Walking]() Original publisher: [Read original article]() Author: Nuno Loureiro Published: 2026-05-27T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [AI Bots]() Tags: [agent](), [ai](), [announcements](), [api](), [apis](), [authentication](), [autonomous](), [benchmark](), [bugs](), [code](), [llm](), [opinion](), [post](), [product](), [reasoning](), [responses](), [sdlc](), [security](), [speed](), [sql]() ### AI overview Snyk argues that continuous offensive security should combine DAST, AI pentesting, and agent red teaming to identify exploitable vulnerabilities before autonomous attackers do. It contrasts heuristic-detectable flaws with context-dependent and chained vulnerabilities. ### Source excerpt Snyk's Continuous Offensive Security unifies DAST, AI pentesting, and agent red teaming to find exploitable flaws -- not just bugs -- before attackers do. Here's why lineage matters. ## How AI Coding Agents Are Changing Product Management Roles DevFeed: [How AI Coding Agents Are Changing Product Management Roles]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-05-14T12:45:08Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [Claude Code](), [Claude]() Tags: [ai](), [ai-coding-agents](), [claude-code](), [coding-agents](), [opinion]() ### AI overview An opinion article argues that AI coding agents are changing product management roles and expectations. It discusses several PM role patterns and suggests that product professionals should adapt to emerging opportunities while acknowledging uncertainty about the future. ### Source excerpt "The skills I cultivate for 15 years are now available for free. ## The Problem of Pedagogy in Advanced Mathematics DevFeed: [The Problem of Pedagogy in Advanced Mathematics]() Original publisher: [Read original article]() Published: 2026-05-11T00:00:00Z Content type: opinion Language: en Sources: [Susam Pal]() Topics: [Mathematics](), [Math and Logic]() Tags: [advanced](), [mathematics](), [opinion](), [students](), [theory]() ### AI overview The article argues that pedagogy remains a serious problem in advanced mathematics. It focuses on graduate-level textbooks whose proofs are often presented as high-level outlines, leaving students and even professional mathematicians to reconstruct omitted intermediate steps. It advocates explanations that are correct, complete, and accessible to reasonably motivated students. ### Source excerpt It is a commonly held opinion that educational institutions could do more to improve the pedagogy of mathematics. This is especially applicable to primary and secondary schools, where students are first exposed to mathematics as a formal subject, along with other new subjects. Poor exposition can turn students away from mathematics for a lifetime. Only the highly motivated ones continue to engage with the subject. This is very unfortunate because mathematics is a beautiful subject and it is filled with wonder. It also teaches rigour in reasoning, clarity of thought and the discipline of constructing arguments from first principles to obtain intricate and often beautiful results. What is perhaps less known is that pedagogy is a problem even for graduate-level mathematics students and professional mathematicians. The proofs in many graduate-level mathematics textbooks are, in my humble opinion, not really proofs at all. They are closer to high-level outlines of proofs. The authors simply do not show their work. The student then has to put in an extraordinary amount of effort to understand and justify each line. Sometimes a 10-line argument in a textbook might expand into a 10-page proof if the student really wants to convince themselves that the argument works. I am not a mathematician, but out of personal interest, I have worked with professional mathematicians in the past to help refine notes that explain certain intermediate steps in textbooks (for example, Galois Theory by Stewart, in a specific case). I was surprised to find that it was not just me who found the intermediate steps of certain proofs obscure. Even professional mathematicians who had studied the subject for much of their lives found them obscure. It took us two days of working together to untangle a complicated argument and present it in a way that satisfied three properties: correctness, completeness and accessibility to a reasonably motivated student. There is a reason why jokes like 'proof by obv ## Tokensparsamkeit for coding assistants DevFeed: [Tokensparsamkeit for coding assistants]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-05-10T00:00:00Z Content type: opinion Language: en Sources: [Nicolas Fränkel]() Topics: [coding](), [Large Language Model](), [Command-line interface](), [Compression](), [Rust](), [Claude Code]() Tags: [agents](), [claude-code](), [cli](), [coding](), [compression](), [llm](), [opinion](), [rust](), [sparsamkeit](), [technical](), [tokens]() ### AI overview This opinion article argues for "Tokensparsamkeit," or token frugality, in coding assistants: using only the tokens needed to achieve comparable results. It discusses compressing prompts and describes a Rust CLI proxy that the author says reduces LLM token consumption by 60-90% on common development commands, with limitations for Claude Code's built-in tools. ### Source excerpt Good engineers make decisions based on data. Most businesses assumed that the more data, the better the decision. Then, several factors put a halt to the hoarding of ever more data. GDPR and its localized counterparts, and the cost of storage. However, before the GDPR came into effect, the Datensparsamkeit approach already existed. Datensparsamkeit is a German word that's difficult to translate properly into English. ## The Revenge of the Data Scientist DevFeed: [The Revenge of the Data Scientist]() Original publisher: [Read original article]() Author: Hamel Husain Published: 2026-03-26T07:00:00Z Content type: opinion Language: en Sources: [Hamel Husain]() Topics: [Data Science](), [Large Language Model](), [AI Development](), [foundation-models](), [SIEM, Security, Observability]() Tags: [ai](), [codex](), [data-science](), [evals](), [llm](), [llms](), [observability](), [openai](), [opinion]() ### AI overview An opinion article argues that data science remains central to AI development even as foundation-model APIs let teams integrate AI without relying on data scientists and machine learning engineers for every project. It emphasizes experimentation, generalization testing, debugging stochastic systems, metrics, and observability in AI harnesses. ### Source excerpt Is the heyday of the data scientist over? The Harvard Business Review once called it "The Sexiest Job of the 21st Century."1 In tech, data scientist roles were often among the best paid.2 The job also demanded an unusual mix of skills: Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician. -- JosH100 (@josh_wills) May 3, 2012 In addition to creating a high-barrier to entry, these skills enabled data scientists to build predicitive models, measure casuality and find patterns in data. Of these, predicitive modeling paid best. Companies later peeled that work off into a new title: Machine Learning Engineer ("MLE").3 For years, shipping AI meant keeping data scientists and MLEs on the critical path. With LLMs, this stopped being the default. Foundation-model APIs now allow teams to integrate AI independently. Getting cut out of the loop rattled data scientists and MLEs I know. If the company no longer needs you to ship AI, it is fair to wonder whether the job still has the same upside. The harsher story people tell themselves: unless you are pretraining at a foundation-model lab, you are not where the action is. In my opinion, training models was never most of the job. The bulk of the work is setting up experiments to test how well the AI generalizes to unseen data, debugging stochastic systems, and designing good metrics. Calling an LLM over an API does not make this work go away. I recently gave a talk titled "The Revenge of the Data Scientist" at PyAI Conf to make that case with examples rather than assertion alone. Below is an annotated version of that presentation. The Harness Is Data Science OpenAI published a blog post on harness engineering that I recommend reading. They describe how Codex worked on a software project for months, autonomously, with agents developing code bounded by a harness of tests and specifications. One detail in that blog post's description of the harness i ## The Golden Ratio of Manager to IC DevFeed: [The Golden Ratio of Manager to IC]() Original publisher: [Read original article]() Author: Daniel Doubrovkine (dblock@dblock.org) Published: 2026-02-04T00:00:00Z Content type: opinion Language: en Sources: [Daniel Doubrovkine]() Topics: [Meta](), [AI Engineering](), [Artificial Intelligence](), [Software]() Tags: [ai-assistants](), [ai-engineering](), [meta](), [opinion](), [organization](), [people](), [teams]() ### AI overview This opinion examines Meta's reported plan for an applied AI engineering organization with up to 50 employees per manager. It argues that flatter structures may reduce unnecessary management layers and that AI assistants are increasing individual contributors' capabilities, while suggesting that managerial roles must continue to evolve. ### Source excerpt In today's shocker, Meta is to "create a new applied AI engineering organization aiming for an ultra-flat structure of up to 50 employees to one manager". Like all software engineers I, too, tend to apply a data-driven, mathematical approach to every problem in the world. Yet I would have chosen a more romantic number and applied the golden ratio: roughly 1.6:1, the proportion that shows up in seashells, galaxies, and every second slide about "natural elegance", rather than 50:1, a measure that feels less like harmony and more like a spreadsheet's idea of efficiency. The idea of flattening an organization is not new and can be a good one. I know plenty of managers who have not done any individual contributor work, code or otherwise, in years. This is particularly striking with former strong coders who are promoted to managerial roles. After 2-3 cycles of promotions they are so far detached from what's happening at the individual-contributor level that they become 100% overhead, spending their entire life in meetings and actively preventing real work from being done. It's natural to want to eliminate layers of such people as they simply don't have any impact. And so, the real news at Meta is that it's fighting its own organization design in which, at least in some teams according to my friends who work or have worked there, people managers are discouraged from doing deep technical work, don't own much beyond process, and mostly serve as reporting-structure placeholders. Another reason to flatten an organization is the introduction of AI assistants that have created a major shift in the capabilities of individual contributors. Two years ago you could maybe find one single "10x engineer" in every team--someone who has dramatically higher velocity than their peers. A good manager would recognize these extraordinary abilities, make such an individual their right hand and technical partner, share the responsibility of advancing a project, create effective mentorship, and h ## Opinion: Impact of AI on Networking Engineers DevFeed: [Opinion: Impact of AI on Networking Engineers]() Original publisher: [Read original article]() Published: 2026-01-29T07:04:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [networking](), [Artificial Intelligence](), [Large Language Model](), [ChatGPT]() Tags: [ai](), [chatgpt](), [llms](), [networking](), [opinion]() ### AI overview This opinion examines how AI may affect networking jobs, distinguishing practical uses from hype. It argues that AI tools, including LLMs, can help experienced practitioners with tasks such as transforming data, finding simple mistakes, and interpreting error messages, while beginners may struggle to detect hallucinations and develop knowledge gaps. ### Source excerpt A friend of mine sent me a series of questions that might also be on your mind (unless you're lucky enough to live under a rock or on a different planet): I wanted to ask you how you think AI will affect networking jobs. What's real and what's hype? Before going into the details, let's make a few things clear: Read more ... ## Open Source Supply Chain Security Gotchas to Avoid in 2025 DevFeed: [Open Source Supply Chain Security Gotchas to Avoid in 2025]() Original publisher: [Read original article]() Published: 2025-12-10T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [supply-chain-security](), [Open Source](), [Security]() Tags: [2025](), [chainguard-christmas](), [chainguard-containers](), [chainguard-libraries](), [gotchas](), [open-source](), [open-source-artifacts](), [open-source-components](), [opinion](), [security](), [supply-chain](), [supply-chain-security]() ### AI overview This commentary uses holiday gift metaphors to identify open source supply chain security pitfalls. It warns that repackaged binaries and image catalogs with questionable provenance can introduce maintenance burdens, technical debt, vulnerabilities, and malicious-code risks, and presents building directly from source as a critical control. ### Source excerpt Skip the "security gift traps." This holiday guide flags common open source supply chain gotchas and shows what to choose instead for speed and trust. [Next page]()