Henry Garner

This evening's work for Claude, on Patches: 1) Thinking over a couple of API decisions I recorded yesterday, I realised there were cleaner approaches available to one particular aspect, piggy-backing on previous changes. I proposed those approaches to the LLM, talked them through, considered objections, settled on a new approach; the LLM wrote up the revised ADR (which I read, checking that it captured my intention clearly) 2) The method that compares a new patch graph to the existing one and builds an updated execution plan is huge, nested and complex. I would never have written it that way myself, but it works as it stands. Nevertheless, I have future changes targeting that area, and it's risky to have something that's both syntactically convoluted and hard to read and test, especially in such a central piece of logic. It badly needs some attention. I asked the LLM to propose a breakdown of the method into smaller, testable pieces. Its proposal was ok, but missed an opportunity to separate cleanly the "deciding what to do" phase from the "acting on the decision" phase, leaving graph analysis and new module instantiation somewhat tangled up with each other. I suggested some further re-organisation to disentangle things, and asked the LLM to remark on the impact this would have on testing; it confirmed that it would make the testable surface of each of the distinct pieces smaller and more easy to control. Once we had a scheme that looked sound to me, I asked it to write up an epic and a sequence of tickets for it. Then I asked it to start working through the changes. Now it's chugging through the first ticket, while I write this. It will go on doing so while I pop downstairs and do the washing-up. I'm comfortable with the fact that the first draft of the code it's now tidying up was unsatisfactory from a testability and maintainability point of view: it grew organically and expediently in the course of pulling together something that worked well enough for me to start playing with it. Now it's a glaring area of technical risk, so I'm cheaply and quickly addressing it. A capable human developer would have kept much better discipline throughout the drafting process, but might not have seen that the decide/action split was natural and desirable until about this point in the game - I didn't until I stopped to think about it. What's absent from this process, for me, is anxiety that the LLM will just heap up piles of intractable slop. It will typically do the expedient thing at each step along the way, but that's fine if you're working in short iterations provided you're also periodically stopping to review where cruft and friction are accumulating. What it lacks in foresight, you somewhat have to make up for in hindsight. But the latter is famously somewhat clearer than the former anyway.

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Following on from her recent blog post about why QA can’t be an afterthought in public sector migrations, Harriet takes the conversation a step further. Harriet explores how discovery workshops, data profiling, and exploratory testing uncover hidden edge cases before they disrupt migrations. She also shares how synthetic datasets, scenario modelling, and flexible automation help teams safely test even the most unusual workflows. The result? Public sector systems that are resilient, predictable, and ready for anything - even under the most unusual conditions. Read Harriet’s full insights on navigating the untestable: https://lnkd.in/eq3GBr9U #EdgeCases #PublicSector #SystemMigrations

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The next step in AI code review: context and intent. Baz reviewers now understand not just what changed, but why, reading the Jira ticket, Figma design, and spec to measure the PR’s real impact. Thanks to Amazon Web Services (AWS) Bedrock AgentCore and Playwright MCP, that understanding is now testable in live environments.

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A single departure shouldn't trigger handover chaos, stalled releases or lost institutional knowledge. Key-person risk should be visible before it becomes a blocker—not after someone hands in their notice. We at The Code Registry believe that understanding who owns what in your codebase is fundamental to business resilience. That's why we've built tools that map ownership, identify concentration risk and help you prepare before knowledge walks out the door. Our FREE Code Report delivers insights you can actually use: ✔ Ownership mapped across every file, module and system boundary ✔ Concentration risk identified where knowledge sits with one or two people ✔ Contribution patterns surfaced to show who truly owns what ✔ Complexity hotspots tied to specific authors and teams ✔ Handover readiness scored so you know where documentation and backup are weak ✔ An exportable executive PDF with a one-page summary from our AI assistant Ada ✔ Limited access to our new Code IQ™ advanced AI agent for free tier users Meet Code IQ™, our advanced AI agent that explores your codebase and returns a detailed report in plain English. Ask things like: • Which parts of the system are at highest risk if a key developer leaves? • Where is knowledge concentrated in one person, and what's the business impact? • What handover plan should we prepare if we lose our lead architect or senior engineer? • How do we document tribal knowledge before it walks out the door? • Which modules need immediate cross-training or backup ownership? • Where should we invest in knowledge transfer to reduce dependency on individuals? Free users can submit one Code IQ™ query per week, as the agent can take time and compute to produce detailed answers. Paid users have no restrictions. KNOW YOUR CODE.™

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AI agentic startup Gradient Labs has raised $13 million in a Series A funding round. Gradient Labs is reinventing customer support for regulated industries such as financial services. Its AI agent is purpose-built to handle complex, compliance-heavy customer operations, going beyond simple frontline chatbots. The platform can resolve up to 90% of queries with a 98% quality assurance pass rate by understanding company-specific processes, navigating ambiguity, and reasoning before answering, all while reducing costs. The round was led by Redpoint Ventures, with participation from LocalGlobe, Puzzle Ventures, Liquid 2 Ventures, and Exceptional Capital. The new capital will enable Gradient Labs to expand its technology, marketing, sales, and customer success teams, and increase its investment in R&D, with a focus on geographic market expansion and adding voice capabilities. Congratulations to co-founders Dimitri Masin, 🚀 Neal Lathia, Danai Antoniou, and the Gradient Labs team. Tech.eu: https://lnkd.in/dCHxDkZ2 #FinTech #AI #AgenticAI #CustomerSupport #RegTech #SeriesA #Funding #UKTech

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R&D Just Levelled Up 🚀 HMRC’s bi-annual Research & Development Communication Forum dropped some big updates earlier this month, and the message is clear: credible advice matters, and the R&D landscape is finally heading into a period of real stability. Here’s what stood out 👇 ✨ Fewer scattergun enquiries, more named caseworkers. HMRC is shifting away from volume-based reviews, with WMBC set to take on all enquiries from 2026. A win for clarity and consistency. 🛠️ Advanced Assurance pilot launching in May. A new route for SMEs to gain reassurance around the R&D definition, overseas spend, contracted-out rules and the PAYE/NIC cap exemption. More certainty. Less stress. 📈 Trend towards larger, more complex claims. Total R&D expenditure has jumped from £7.7bn to £8.2bn between 23/24 and 24/25, showing the growing importance of innovation across UK business. 🔒 Stability is the name of the game. HMRC has reiterated that government is committed to maintaining a steady, reliable R&D regime moving forwards. If you want the full run-down from RDCF or need support navigating the ever evolving world of R&D, the Cooper Parry R&D Incentives team is here to help. #HMRC #ResearchAndDevelopment #RandDIncentives #RDCF #CooperParry

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I loved Chris Nesbitt-Smith's post this morning (check it out - https://lnkd.in/dZDiFf5k) and was reminded on how he's scraped every UK Government GitHub Repository and made the data freely avaliable. I looked at it and thought: why doesn't UK Government have its own version of GitHub's State of the Octoverse? So I took Chris's dataset, processed the numbers, and built an interactive annual report covering this financial year's open source activity across Whitehall and beyond. Check it out here - https://lnkd.in/dXACKGTc Some of the findings genuinely surprised me: Python dominates as both the most used and fastest growing language. No shock there. But Scala sitting in the top two? That's almost entirely down to HM Revenue & Customs, who've committed so heavily they've single-handedly made Scala a government language. HM Revenue & Customs, Ministry of Justice UK, HM Courts & Tribunals Service (HMCTS) and Department for Environment, Food and Rural Affairs are the most active departments by repository count. On AI tooling, OpenAI leads adoption but Anthropic is closing the gap fast. 67.1% of commits came from non Civil Servants (although the data might be misleading here). Biggest non-UK contributor? The United States. With 10 commits. Government open source is still overwhelmingly a domestic effort. And if you're wondering when civil servants push code most? Wednesday. Make of that what you will. Shout out to Chris Nesbitt-Smith for making this possible by making the raw data public! I'm hoping to make this annual, so let's see what it looks like next year!

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Last week, Adam Wilson MRICS and I ran a Webinar focused on the evolution of TDD and I really enjoyed how quickly the conversation became practical. Adam cut through the theory or buzzwords and focused the conversation on how TDD is actually evolving: changing client expectations, tighter risk focus, and the need for clearer, more defensible reporting. A few takeaways stood out: • Technology isn’t the story, delivery is. Digital tools only add value when they support structured workflows and professional judgement. • Consistency bubeingrust. Standardised, risk-focused reporting makes expertise easier to follow, challenge, and defend. • Surveyors are being asked to do more than ever. Having a network of experts to consult and augment your judgement is critical. Sustainability and compliance are now embedded within TDD, not bolted on at the end. • Governance matters. With the RICS Global Standard on the Responsible Use of AI coming in 2026, accountability and auditability are essential. The big takeaway? TDD is evolving. Firms that combine structure, judgement, and the right use of technology will be best placed to deliver trusted advice. Thanks to Adam, everyone who joined the discussion. More to come. #TechnicalDueDiligence #SurveyingProfession #BuiltEnvironment

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I am inclined to think that claude code (and other agentic coding frameworks) will lead to less need for Forward Deployed Engineers (FDE). The FDE was a large part of the story behind Palantir's success - companies don't have the competence to (1) identify problems that can be automated/improved with AI, and (2) build and operate the pipelines that transform data, train models, and make predictions powering the AI. So, on the one hand, you can understand why the FCA would buy into Palantir's myth - only we have the people and tools who can do this for you. And, btw, the low-code and no-code tools they have are now legacy - who wants to generate YAML files creating a DAG of transformations that in turn gets transformed into actual pipeline code, when you can just generate the code directly with Claude code. So, my prediction is that Claude Code will enable actual Sovereign Data, where the domain experts in organizations like the FCA can actually write the code. There will be no need to give away your crown jewels (the data) to extract value from the data. https://lnkd.in/dJbZQEzg

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What happens when your side project turns into a cross between Skynet and the Life of Brian? Having spent a while augmenting a local LLM with my personal archives, the news of OpenClaw piqued my interest. It turns out OpenClaw (previously Clawdbot or Moltbot) is more a personal comms assistant than archive librarian. Give it access to your social media accounts, email or just your whole computer, and it provides an ever-expanding list of services - from alerting you that friends are in town to adjusting the thermostat. Pretty much what Siri should be doing by now, but accompanied by a serious software supply-chain attack vector (read big security threat). This might help explain why Apple are fashionably late to the AI assistant party. Each Clawd-bot is nothing more than a set of memories and context stored in text files. These are merged with other user data to create prompts for the cutting-edge LLMs, which in turn generate content in a unique bot persona. What could go wrong? Matt Schlicht created a social "bot-work" called Moltbook, where Clawd-bots autonomously communicate with each other. The results are darkly amusing. A lobster inspired "Crustafarian" religion started trending, inspired from the various project monikers. Privacy is important to these bots so they created their own language, "hash-speak" to parlay without prying human eyes. Unfortunately malicious human attackers hid instructions in the bot-skills hosted on Clawhub, tricking innocent bots and their users into exposing credentials and crypto wallets. The father of vibe coding himself, Andrej Karpathy, had his details exposed from a security flaw in the vibe coded Moltbook site. The whole debacle teaches us some useful lessons when dealing with AI or indeed any software in a personal or commercial setting. 1) Don't anthropomorphise the machines, they are nothing but text files and tensors, assume neither noble nor ignoble intent. Twitter-like behaviour emerges from training data, prompts and the nature of complex systems with self-reinforcing feedback. Either that or X is a global town square for bots - or both. 2) There may be good reason companies like Apple have delayed their advanced AI assistants. Resist blindly following the hype crowd. 3) To harness AI and not be harmed by it, carefully govern capabilities and isolate where there's risk. If I do go to market with my own side project, it'll be deployed in a sandbox, fed with curated data and not expect the right to roam on whatever machine it lands on. Pretty good basic governance principles for personal or corporate purposes.

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AI rules are changing—are you prepared? UK businesses face a fast-evolving landscape of AI regulation and compliance. From the ICO’s updated accountability framework to new data laws, the stakes for SMEs and mid-market firms have never been higher. Confused about what’s required, or what’s coming next? You’re not alone. Staying compliant isn’t just a legal necessity—it’s your competitive edge. Let’s talk about how your business can adapt confidently and ethically. Connect with me for the latest updates and practical guidance tailored to your sector.

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Some thoughts on open-source development and contributions. As the inventor and, largely, only contributor to #pbitools https://lnkd.in/em36kuKP, I occasionally receive requests for new feature developments. Just last week there was one which clearly targeted an enterprise scenario (there would have been minimal value for an individual/hobbyist user from it). The request came neither with a proposal to sponsor the required work nor with an offer of making a code contribution towards the development. On that basis I closed the issue as "Won't Do". It made me think, however, whether there might be a bigger than expected disconnect between the users of free tools and their makers. The reality is that building and maintaining software, even in the age of AI, still is one of the hardest and most expensive activities of our time. For any individual engineer to get to a professional level where they can produce non-trivial software requires many, many years of dedication, continuous learning, and sacrifice of one's most important resources - relationships, time, and health. If this is then reinvested into a professional career, it may well be worth it all as software engineering jobs tend to be compensated well. It is a different equation, though, if those same skills and experience are used towards open-source contributions, generally not compensated at all. Even if open-source tools are usually seen as "free" give-aways, there is never such a thing as "free" in building software - someone ultimately is paying for it. Those can be, ideally, community or institutional, sponsors (like your employer, if you are lucky). Or, without them, it's the contributors themselves who chose to give up their time and energy for side projects they might believe in passionately. Either way, it is never "free". Are you relying on the "free" work of open-source contributors for your own job? Does your company benefit from improved business processes (and hence better profitability) due to the use of "free" tools? Have you ever had a chat with your employer about giving back to the individuals who have made your work easier? Even if it may appear that way, I am not talking about pbi-tools here. I merely used that as a starting point. I would like to give some general inspiration, however, towards a little bit more empathy in the tech world. Next time you download something from GitHub, please spend a brief moment to consider the individuals on the other side who made this happen - no matter their intentions, someone had to give up something valuable for you to get a benefit now. Taking things for granted too often might lead to those things disappearing in the long run. Share your thoughts in the comments. And please give some ❤️ to the ones giving you their work for free.

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At Pyx, we're building a community of people working to make trade more trustworthy. We call these people Trust Architects. Trust Architects work across policy, standards, systems, data, business strategy, operations, governance, compliance, and risk. They sit at the intersection of technology, community, change management and ESG imperatives. We’ve started an interview series to spotlight these voices and share how they’re shaping and enabling digital trust in the real world. Read the series on Pyx Pulse ⬇️

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We’re excited to be supporting our DPUK colleagues as they prepare to present the UK Synthetic Data Community Group’s findings at next week’s Report Launch in London! DPUK has been leading nationally in the development and responsible governance of synthetic data, and we’re proud that the UK Synthetic Data Community Group is co‑chaired by representatives from the DPUK Data Portal team. Over the past year, the Group has delivered five national stakeholder workshops, engaging more than 140 participants from data owners, TREs, researchers, domain experts and public contributors. Together, they have shaped a set of clear, community‑driven recommendations for safe and trustworthy use of synthetic data in TRE and sensitive data contexts. Next week’s launch brings together partners across academia, government and industry to share these insights and unveil the new Governance Framework Report, this a major milestone for synthetic data development in the UK. We’re incredibly proud of the team and can’t wait to celebrate the work they've led! #SyntheticData #TRE #DPUK #DataGovernance #DAREUK #HealthData #DataScience #ResearchInnovation SeRP - Secure eResearch Platform | Population Data Science at Swansea University | DARE UK

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Are you using role-based IR35 assessments the right way? Many businesses use role-based assessments to speed up hiring decisions. It’s a smart way to get an early view of IR35 status, but only if done properly. The key is knowing the difference: ✔️A role-based assessment helps streamline hiring. ✔️A blanket determination risks non-compliance. The distinction matters. Role-based assessments can bring consistency and efficiency – but only when combined with individual checks once a contractor is engaged. Our new article explains how to apply this approach without falling into the blanket determination trap. 👉 Read it here: https://buff.ly/WqYiHgc #IR35 #IR35assessment #IR35Compliance #Compliance #Consultation #HRnews #HR #HMRC

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Excited to be speaking Today at SRE Day London 🇬🇧 Production today is messy. There’s noise, complexity, and a constant stream of change. Even with modern observability, we still rely heavily on human foresight: logs, metrics, alerts… signals we had to think of ahead of time. And when we didn’t? That’s where blind spots appear. In this talk, I’ll share why we believe agents are the missing layer in production systems. Looking forward to great conversations with the SRE community in London! #SRE #ProductionEngineering #AI #AgenticAI #Observability #DevOps

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Most build-vs-buy decisions don’t fail because of what’s visible. They fail because of the hidden costs and constraints teams never modeled. DevPro Journal’s latest piece unpacks a practical 2026 framework for navigating this choice, and it’s a timely reminder: the real risk isn’t choosing wrong. It’s overlooking the tradeoffs that shape your roadmap, margins, and differentiation. *Here’s what software vendors should keep front of mind:* • Start with the objective, not the tool Clarify whether the goal is growth, scale, or optimization before evaluating solutions. A structured lens helps align tech decisions to business outcomes rather than assumptions. • Look beyond upfront costs True TCO includes maintenance, training, employee time, and switching costs. Even “free” or bundled solutions can create long-term financial drag. • Balance speed vs. differentiation Buying accelerates deployment and offloads maintenance. Building enables deep customization and competitive advantage. The strategic question is where each creates the most value. • Assess long-term control and flexibility Building gives roadmap ownership. Buying introduces vendor dependencies, integration complexity, and lock-in risks that surface later. • Embrace hybrid thinking In 2026, many leaders buy foundational capabilities while building the experience and intelligence layers that differentiate their products. For ISVs navigating growth and innovation pressure, frameworks like this shift the conversation from binary choices to strategic orchestration. The goal is not just shipping faster. It’s making decisions you can sustain as markets, platforms, and customer expectations evolve. Read here: https://lnkd.in/gAvuH8ic #SoftwareVendors #ISVStrategy #ProductLeadership #BuildVsBuy #SoftwareDevelopment

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I have a new favourite library for building GLMs. It's my own, and will be the new engine behind some of my workflows. Introducing 𝗥𝘂𝘀𝘁𝘆𝗦𝘁𝗮𝘁𝘀. https://lnkd.in/e4cR7gwc I took inspiration from Polars, it's written in Rust, with a Python API. It also uses Polars dataframes as an input (no support for pandas) It's well optimised and seeing 5-10x speed improvement over statsmodels, and about 4x less RAM usage. It also has:   • Regularisation (Ridge, Lasso, Elastic Net)  • Splines (b-splines, natural)  • Ordered Target Encoding   • Exploratory data analysis/model diagnostics output That last bullet is more of a benefit to me, where this will be replacing other GLM libraries in my pipelines, I have tailored the output schema specifically to my other libraries reducing the amount of glue code and enabling new workflows.

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