Ewan Silver

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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If your company still occasionally spends from founders’ or employees’ personal cards, it doesn’t just mean you have “scrappy ops.” It means: • untracked risk • poor expense hygiene • silent resentment within the team • and a finance team doing archaeology every month Personal cards aren’t a workaround. They’re a signal that your spend system is broken. Period. Get corporate cards, especially the ability to create and manage as many virtual cards as you need from a single dashboard.

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When did you last ask your engineering team not what they have tested, but what they can prove. If you cannot remember, that is already an answer. Most technology organisations conflate these two things. Testing tells you the system worked in the cases you tried. Proof tells you whether it can fail in ways you have not tried. At scale, in regulated environments, the distance between those two statements is where institutional risk accumulates quietly. Here is the question that carries the most weight for me. How do you prove your system is calculating interest correctly across your entire book of accounts. Not on a sample. Across every account, under every combination of product type, rate tier, promotional period, and balance behaviour, simultaneously, at production scale. A systematic error at account level can be invisible for months. The aggregate numbers reconcile. The monitoring is silent. The team has confidence. The error does not announce itself. It accumulates. When a regulator finds it before you do, the conversation changes entirely. You are no longer presenting a post-incident review. You are explaining why your quality assurance was insufficient to detect a systematic error affecting customers at scale. This is not an engineering problem. It is a governance problem wearing engineering clothes. At the scale most institutions are now operating, this question is not optional. It is overdue. Read the Post 2 of Season 2 of Systems and Sutras delve into this further https://lnkd.in/etZDPRcF Sutra Confidence scales. So does the cost of misplacing it.

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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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SECR and Scope 3 reporting are designed to help. But right now? They mostly just measure pain — they don’t solve it. Lessr isn’t a reporting tool. It’s a visibility layer — for ops teams that want to take action, not just fill in a spreadsheet. If we do our job right, your next SECR submission becomes a side-effect — not a scramble. #SECR #Scope3Ready #ActionableData

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BBC Panorama’s latest investigation is a sobering reminder of how easily credit can go wrong - even when intentions are “responsible”. https://lnkd.in/e2cWxqrv The programme highlights cases where individuals trying to improve their financial position were instead nudged towards more high-interest credit, deeper debt, and prolonged financial distress. What stands out is not just individual behaviour, but systemic decisioning gaps: - Credit offers triggered by improving scores, without sufficient context or vulnerability signals - Automated limit increases that fail to recognise financial stress or life events - Minimum-payment structures that unintentionally reinforce long-term debt - Fragmented accountability between credit data, marketing, and lending decisions These are not failures of intent- they are failures of how decisions are designed, governed, and monitored. At DCS, we help lenders address these issues through Credit Studio, enabling a fundamentally different approach to credit risk decisioning: https://lnkd.in/e9_M3JVQ - Holistic, real-time decisioning that combines affordability, behaviour, vulnerability indicators, and lifecycle context — not just credit scores - Early-warning and intervention strategies that pause, redirect, or support customers rather than automatically extending more credit - Policy-driven guardrails that ensure marketing, credit limits, and offers remain aligned to risk appetite and regulatory expectations - Full explainability and audit, allowing lenders to evidence why a decision was made - and why an offer was not. - Most importantly, Credit Studio allows lenders to simulate and test outcomes before deploying them, helping avoid unintended consequences that only surface once customers are already in difficulty. Responsible lending today isn’t just about saying “yes” or “no”. It’s about knowing when to slow down, intervene, or offer help instead of more credit. That requires decisioning platforms that are governed, adaptive, and human-aware - not just score-driven. #CreditRisk #ResponsibleLending #Decisioning #FinancialWellbeing #Banking #RiskManagement #Pega #wearedcs Pradeep Warden Nanjundan Chinnasamy Flavio Gomes Alasdair M. Arnold Koudijs Rob McLeod David Hampton Douglas Hampton

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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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I’m delighted to be speaking at FINOS Developer Day @ apidays London on September 22nd! My session, “From Model to Machine: Open Standards and Automation in Post-Trade”, will explore how the FINOS Common Domain Model (CDM) can move beyond theory to power real-world automation. Post-trade processes should be as simple as contracts: rights, obligations, fulfilment. But in today's systems, they often unravel into a tangle of APIs, messages, inconsistent data and reconciliations. I’ll share how we at Tokenovate are tackling this complexity — using CDM as a declarative foundation, APIs as execution interfaces, and orchestration to automate trade lifecycle. If you’re curious about the future of CDM, open standards, and automation in capital markets, I’d love to see you there. More info on apidays London: https://lnkd.in/eZmgPWb6 #FINOS #apidays #OpenSource #APIs #PostTrade #CDM #CapitalMarkets #tokenovate

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Billed as a “powerful new Fintech platform,” GFTM LTD (Good For The Money) has launched to help UK builders’ merchants identify bad payers in real time — at no cost to those who share data. See below for further insight from CEO and co-founder Jeremy Price: https://lnkd.in/e_AMn626 #buildersmerchants #buildingmaterials #baddebt #badpayers #cashflow #supplychain

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This isn’t just any report… it’s the future of property reporting. The new InfoTrack UK Property Report takes innovation in conveyancing a step further and brings together lease reading, mortgage deed checks, and LPE1 extraction into a single, client-ready document. With the Magic Eye, every check is just one click away - saving time, reducing risk, and making compliance effortless. For conveyancers, this isn’t just an upgrade. It’s a smarter, more intuitive way of working. #Conveyancing #PropertyLaw #LegalTech #Innovation #FutureOfLaw #PropTech

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If you are a non-technical founder and you need to interview tech folk. Here are a few of the tips that I use on the regular. 'cause, after 30 years new stuff comes out all the time so I always have to interview people who know more than I do. 👉 Ask them about tech they dislike. Ask them why they dislike it. You know when you hear it. Does their answer make sense or does it sound like they are just complaining? You can hear signs of emotional intelligence. Just ask me about Javascript sometime and I'll give you a masterclass in detecting red-flags. 👉 Ask them to describe what they did the last time they inherited a project or codebase If they burned it to the ground, dig deeper and ask them why. What made them think the tech wasn't good enough. Ask for some concrete examples. "It's not my thing" and "I hate Javascript" are not good enough answers. Make them educate you, that will their continual job on your team. (p.s. since my friend Benny Mathew thinks that emojis are a sure sign of AI, I want to assure my listeners that this #handwritten. See, no em-dashes!) Here is a picture of me not interviewing anyone. See how happy I look?

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And now for some readings of user level RCU .. and a landmark paper that led to the implementation of liburcu - the user space RCU library, a Christmas Day evening read .. Why RCU is difficult in user space ? RCU, particularly its high-performance Quiescent-State-Based Reclamation (QSBR) variant, is easier in kernel mode because the kernel scheduler automatically detects quiescent states whenever a CPU context-switches, enters user mode, or idles, allowing grace-period tracking without any explicit cooperation from kernel code. In user mode, applications lack this built-in mechanism, so threads must explicitly register and periodically report quiescent states (e.g., by calling specific functions), imposing invasive global constraints on the entire application. These requirements make user-level RCU harder to adopt broadly, as they complicate library design and require modifications to all potentially reading threads, which is impractical in many user-space programs. User level implementations of RCU : This paper contributes to user-level RCU by formally describing efficient and flexible implementations that overcome the limitations of prior approaches, which either imposed high read-side overhead or severely restricted application design. It presents multiple classes of RCU (including QSBR, memory-barrier, signal-based, and bullet-proof variants) with detailed algorithms, performance analysis, and comparisons to locking, directly forming the foundational basis for the liburcu library's core flavors and enabling its widespread adoption in user-space applications.

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Spoke at the London Microservices Meetup yesterday - on the topic of keeping things up to date. There always seems to be something that needs to be updated or migrated, particularly if you're a platform team. Getting good at managing these sorts of projects is well worth it. They may not be exciting but they are essential, and if you can get through a migration without everyone hating you, that's a badge of honour! I very much enjoyed Eddie's talk at the same event on Building AI Agents, a great roundup of the options available with lots of practical examples. Thanks to Oliver Short, Mark Short and the SEEKR team for inviting me and hosting the event. If this sounds interesting to you, I quite often give talks at organisations, either as a one-off or as part of a larger consultancy engagement. There's a list of recent talks on my website at https://lnkd.in/eJ9tJrwE

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"I’m a big believer that the more we use words and natural language as technical devices in software engineering, the more relevant insights from the humanities will become."

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This morning, Philip Thornton and I had a quick chat about my Claude Code Mastery course and both of us were hit with a realisation. Many organisations have mandated the use of AI tooling. Especially in software development. Very few of them however are experiencing the expected benefits. What we realised is that we have forgotten that what we are asking people is to change! With change comes decades of learnings brought to us by the change management and adoption related sciences. Your teams won't suddenly transform over night once you place the AI tool in their hands. They need training, space, and time. Training to understand the tool and see what's possible. Space to experience what's possible and safely fail. Time to practice. Deliberately practice. To become decent users of these tools that are powerful with the right guardrails, dangerous without them, and mediocre because of fear and lack of understanding. So when giving your people the tools, ensure that you are also giving them the training, space, and time they need to master them. If you need help, check out my Claude Code Mastery class which has the training, space, and time built into its design.

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Great summary from Steven on GOV.UK Wallet — and a thoughtful discussion on using public sector–issued credentials in the private sector. ✅ Presenting your ID for age verification during a purchase? Covered. ❓Presenting a ticket bound to your ID — what will that look like in practice? The GOV.UK ecosystem keeps public and private sector-issued credentials separate, with public ones usable in the private sector via intermediaries — a move that also helps address the scale challenge EUDI wallet ecosystem will face. But what does that mean for presenting both public and private credentials together? Will intermediaries orchestrate and need to be aware of both — or will that mean two separate presentations/QR scans for privacy reasons? It’ll be interesting to see how this unfolds. #DigitalIdentity #GOVUKWallet #EUDI

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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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