Developer Productivity Metrics

𝐘𝐨𝐮 𝐃𝐨𝐧’𝐭 𝐍𝐞𝐞𝐝 𝐋𝐨𝐧𝐠𝐞𝐫 𝐁𝐫𝐞𝐚𝐤𝐬 — 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐎𝐧𝐞𝐬 ☕ I used to take coffee breaks just to scroll my phone, check notifications, and mentally disconnect. Spoiler: I came back more distracted than refreshed. Working 10+ hour days as a Research Analyst taught me this: how you spend your break determines how well you work after it. So I stopped taking default breaks — and started using them intentionally. Here’s how I now make 15-minute coffee breaks actually count 👇 📍𝗠𝗼𝘃𝗲. 𝗘𝘃𝗲𝗻 𝗮 𝗹𝗶𝘁𝘁𝗹𝗲. Quick walk. Light stretch. Just getting away from the desk boosts blood flow and clears mental fog — science backs this. 📍𝗡𝗼 𝘀𝗰𝗿𝗲𝗲𝗻𝘀. 𝗡𝗼 𝘄𝗼𝗿𝗸 𝘁𝗮𝗹𝗸. I used to check LinkedIn or emails “for a sec” — that didn’t help. Now, I use breaks to disconnect fully — so I can return focused. 📍𝗥𝗲𝗳𝗹𝗲𝗰𝘁 𝗼𝗿 𝗥𝗲𝘀𝗲𝘁. Sometimes I take 2 mins to revisit my task list, reprioritize, or ask: What’s the one thing I need to finish today? It keeps me aligned and avoids the afternoon drift. 📍𝗙𝘂𝗲𝗹 𝘄𝗶𝘁𝗵 𝗽𝘂𝗿𝗽𝗼𝘀𝗲. Not just coffee. Hydration + light snacks = energy boost. Caffeine helps, but balance matters more. Bottom line? A well-used break can add hours of productivity to your day. It’s not about pausing work — it’s about recharging with intention. How do you make the most of your breaks? I’m always up for better ideas — drop yours 👇 #WorkSmart #CoffeeBreakWisdom #ProductivityTips #FocusAtWork

After Chain-of-Thought and its successors comes Chain-of-Draft. This is inspired by human cognitive processes, which typically use very concise concepts as thinking steps. The result is efficiency improvements of up to 13x and latency reduction of 76& while maintaining quality similar to Chain-of-Thought. In addition to driving LLM efficiency, I believe these outcomes can in fact help humans enhance their own cognitive processes. Observing these results can inform our metacognition in considering the nature of our reasoning steps. This interesting research, along with code, comes from Zoom, which is going primary AI research. Key insights from the paper: ✍️ Chain of Draft (CoD) cuts reasoning costs by 80% with minimal impact on accuracy. Unlike traditional Chain of Thought (CoT) prompting, which generates detailed step-by-step explanations, Chain of Draft (CoD) focuses on minimal yet informative intermediate steps. In tasks like GSM8K, CoD reduces token usage by 80% while maintaining over 91% accuracy—just slightly below CoT’s 95% but with far lower latency and cost. This makes it ideal for real-time applications where efficiency is critical. ⚡ Faster responses with CoD yield up to 76% latency reduction. CoT's verbosity increases response time, but CoD’s concise reasoning steps significantly cut delays. In arithmetic reasoning tasks, CoD reduces response latency by 76.2% for GPT-4o and 48.4% for Claude 3.5 Sonnet. This efficiency boost makes CoD a strong alternative for large-scale LLM deployments where speed matters. 📉 CoD struggles in zero-shot and small model settings. Without few-shot examples, CoD’s performance drops significantly—only improving accuracy by 3.6% over direct answering in Claude 3.5 Sonnet. Smaller models (under 3B parameters) also show weaker results, suggesting that CoD-style reasoning isn’t well-represented in training data. Fine-tuning on compact reasoning data could help bridge this gap. 💡 LLMs don’t need long explanations to reason effectively. CoD challenges the assumption that detailed reasoning steps are necessary for accuracy. By distilling thought processes into compact drafts, it achieves similar results with far fewer tokens. Future optimizations could combine CoD with other latency-reducing techniques, making LLMs more efficient without sacrificing interpretability.

My work is very busy at present. I have a demanding schedule of coaching appointments, workshops, webinars, and learning design deliveries, as well as administrative tasks. So I took yesterday off to ski. Stepping away regularly from work isn't just enjoyable; it’s essential. Research shows that intentional breaks — especially active ones — deliver powerful benefits that enhance our performance and well-being: • 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘆: Our brains operate on an attention budget that depletes throughout the workday (you may notice, for example, that you are more capable of focused productivity in the morning than at the end of the day). Even brief breaks can replenish this resource. During physical activity, different neural pathways activate, allowing overused cognitive circuits to recover — like resting one muscle group while working another. • 𝗠𝗲𝗻𝘁𝗮𝗹 𝘄𝗲𝗹𝗹-𝗯𝗲𝗶𝗻𝗴: Breaks function to interrupt the cycle of stress accumulation. Physical activity in particular triggers endorphin release and reduces cortisol levels, creating a neurochemical reset. Research from Wendsche et al. published in the Journal of Applied Psychology found that regular work breaks were consistently associated with lower levels of reported burnout symptoms. • 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗿𝗲𝗷𝘂𝘃𝗲𝗻𝗮𝘁𝗶𝗼𝗻: Studies in occupational health show that the extended periods of continuous sitting that characterize professional work negatively impact cardiovascular health and metabolism. Active breaks counteract these effects by improving circulation, reducing inflammation markers, and maintaining insulin sensitivity — benefits that persist when you return to work. • 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝘀𝗵𝗶𝗳𝘁: Psychological distance from problems activates different regions of the prefrontal cortex. This mental space triggers  an incubation effect wherein our subconscious continues problem-solving while our conscious mind engages elsewhere. Many report solutions crystallizing during or immediately after breaks. • 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 𝗯𝗼𝗼𝘀𝘁: Research published in the Journal of Experimental Psychology found that walking increases creative ideation by up to 60%. Additionally, exposure to novel environments (like mountain vistas) activates the brain's novelty-recognition systems, priming it for innovative thinking. • 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆: A study in the journal Cognition found that brief diversions improve focus during extended tasks. Research from Microsoft’s Human Factors Lab revealed that employees who incorporated strategic breaks completed projects 40% faster with fewer errors than those who worked straight through. The irony? Many of us avoid breaks precisely when we need them most. That urgent project, deadline pressure, or busy season seems to demand constant attention, yet this is exactly when a brief disconnect delivers the greatest return. #WorkLifeBalance #Productivity #Wellbeing

LLMs just got a lot cheaper to run (and "smarter" too) We've been living with a fundamental trade-off in AI: better reasoning means burning more compute. Want your model to solve harder problems? Just have it "think" longer, generate more possibilities, vote on the best answer. It works, but at a cost that's often not proportional to the gains. A new paper from Meta introduces DeepConf, and it might just change that equation. Their key assumption is this: LLMs actually know when they're confident about their reasoning. By tracking this confidence signal during generation, the system can detect when a line of reasoning is going off the rails and simply... stop. No need to generate thousands of tokens for an answer the model already knows is wrong. On challenging math competitions like AIME 2025, DeepConf achieves 99.9% accuracy while reducing token generation by up to 84.7% compared to standard approaches. That's not a typo - we're talking about 5x fewer tokens for better results. So, how does it work? As the model generates each token, it computes a rolling "confidence window." When confidence drops below a threshold - often signaling confusion or circular reasoning - generation stops immediately. The system then aggregates only the high-confidence reasoning paths for the final answer. Here's why this matters beyond just cost savings: it suggests LLMs have better "metacognition" than we thought. They can, in effect, recognize their own confusion. This might mean more reliable AI systems that know when to be uncertain, when to stop and reconsider, and when to confidently proceed. This would have downstream effects: Companies struggling with inference costs could see dramatic reductions. Real-time applications that were previously impossible due to latency become feasible. And we might be seeing the emergence of AI systems that can better calibrate their own certainty - an important step toward more trustworthy AI assistants. We're watching the field optimize itself in real-time. What other "obvious in hindsight" improvements are we missing? ↓ 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐤𝐞𝐞𝐩 𝐮𝐩? Join my newsletter with 50k+ readers and be the first to learn about the latest AI research: llmwatch.com 💡

The best-performing software engineering teams measure both output and outcomes. Measuring only one often means underperforming in the other. While debates persist about which is more important, our research shows that measuring both is critical. Otherwise, you risk landing in Quadrant 2 (building the wrong things quickly) or Quadrant 3 (building the right things slowly and eventually getting outperformed by a competitor). As an organization grows and matures, this becomes even more critical. You can't rely on intuition, politics, or relationships—you need to stop "winging it" and start making data-driven decisions. How do you measure outcomes? Outcomes are the business results that come from building the right things. These can be measured using product feature prioritization frameworks. How do you measure output? Measuring output is challenging because traditional methods don’t accurately measure this: 1. Lines of Code: Encourages verbose or redundant code. 2. Number of Commits/PRs: Leads to artificially small commits or pull requests. 3. Story Points: Subjective and not comparable across teams; may inflate task estimates. 4. Surveys: Great for understanding team satisfaction but not for measuring output or productivity. 5. DORA Metrics: Measure DevOps performance, not productivity. Deployment sizes vary within & across teams, and these metrics can be easily gamed when used as productivity measures. Measuring how often you’re deploying is meaningless from a productivity perspective unless you’re also measuring _what_ is being deployed. We propose a different way of measuring software engineering output. Using an algorithmic model developed from research conducted at Stanford, we quantitatively assess software engineering productivity by evaluating the impact of commits on the software's functionality (ie. we measure output delivered). We connect to Git and quantify the impact of the source code in every commit. The algorithmic model generates a language-agnostic metric for evaluating & benchmarking individual developers, teams, and entire organizations. We're publishing several research papers on this, with the first pre-print released in September. Please leave a comment if you’d like to read it. Interested in leveraging this for your organization? Message me to learn more. #softwareengineering  #softwaredevelopment #devops

Most CTOs can't answer this question: "Where are we actually spending our engineering hours?" And that's a $10M+ blind spot. I was talking to a CTO recently who thought his team was spending 80% of their time on new features. Reality: They were spending 45% of their time on new features and 55% on technical debt, bug fixes, and unplanned work. That's not a developer problem. That's a business problem. When you don't have visibility into how code quality impacts your engineering investment, you can't make strategic decisions about where to focus. Here's what engineering leaders are starting to track: → Investment Hours by Category: How much time goes to features vs. debt vs. maintenance → Change Failure Rate Impact: What percentage of deployments require immediate fixes → Cycle Time Trends: How code quality affects your ability to deliver features quickly → Developer Focus Time: How much uninterrupted time developers get for strategic work The teams that measure this stuff are making data-driven decisions about technical debt prioritization. Instead of arguing about whether to "slow down and fix things," they're showing exactly how much fixing specific quality issues will accelerate future delivery. Quality isn't the opposite of speed. Poor quality is what makes you slow. But you can only optimize what you can measure. What metrics do you use to connect code quality to business outcomes? #EngineeringIntelligence #InvestmentHours #TechnicalDebt #EngineeringMetrics

In 2023, a lawyer used ChatGPT to cite six federal cases in Mata v. Avianca Airlines. None of them were real. The judge wasn’t amused. A huge amount of legal labor is pattern recognition… → spotting risk in contracts → flagging clauses → checking compliance → classifying documents All of which AI is surprisingly good at. 1. Contract Review = AI’s current stronghold Startups like Kira Systems, Luminance, and Evisort are already being used by major firms and in-house legal teams to review thousands of contracts in minutes. How it works: - NLP models are trained on millions of legal documents - They extract entities (parties, dates, obligations) - Flag unusual clauses or missing terms - Compare contracts to templates and playbooks - Suggest standardized language These systems don’t “understand” law like a human, but they do spot patterns with superhuman speed and consistency. Some use cases: M&A due diligence, lease abstraction, procurement review, NDAs and vendor agreements. 2. AI legal assistants Companies like Harvey (built on GPT-4 and backed by OpenAI and Sequoia) are building “AI co-counsel” tools for major law firms like Allen & Overy and PwC Legal. These tools can: - Draft memos, emails, and summaries - Translate legal language into plain English - Review case law and generate first-pass legal research - Answer questions about internal policies or past cases using retrieval-augmented generation Some corporate legal departments are now using LLM-powered chatbots to field internal questions like “Can we onboard a contractor in France?” Most firms still keep a human in the loop, but the productivity gains (especially for junior attorneys) are real. 3. Legal research Instead of spending hours on Westlaw or LexisNexis, LLMs like CoCounsel (by Casetext) and Ask Sage let lawyers type queries in natural language: “Find cases where a noncompete was struck down in California after 2021” They return relevant cases, key excerpts, and links to full decisions. But… what about ethics, bias, and accountability? Hallucinations: LLMs can still generate fake cases, made-up statutes, or misquote real ones Bias: training data often reflects real-world legal inequities so models might encode racial, gender, or class bias in sentencing, surveillance, or risk scoring Black-box risk: if you can’t explain why the model flagged something, can you trust it? Confidentiality: uploading sensitive legal docs to a public API? Probably not compliant. That’s why most law firms are either building private in-house models, using vetted APIs with strict data policies, or restricting LLM use to low-risk, client-facing tasks. Basically, AI in law isn’t about robots arguing in court (yet?). It’s about freeing lawyers from boilerplate and speeding up research and review. 👉 I’ve given myself 30 days to learn about AI. Follow Justine Juillard to keep up with me. 17 days to go.

Anthropic says Claude can stay focused, coding, for 30 hours. Bain’s Technology Report 2025 shows 80% of companies see no ROI … Why? Because focus isn’t the problem. Context is. And fixing context means rethinking how and where we use AI across the software lifecycle. Sure, AI can follow clean instructions, especially on a new application build, but it still doesn't understand how one line of code breaks something three levels down. The report found developers are actually 19% slower with AI assistants, they spent more time fixing hallucinations than writing code. Here were some of the other findings from the Bain report: 🔹 Value shows up in narrow tasks but quickly disappears in the large, messy codebases most organizations live with 🔹 Developers expected AI to speed them up. Instead, they spent more time verifying and repairing its outputs 🔹 Community tests showed AI answers are "almost right." But in software, almost right = wrong. And wrong is sometimes harder to fix 🔹 Security teams reported 10x more vulnerabilities in AI-assisted code Sure, Claude's latest findings are impressive, but AI tends to do fine when the problem is neat and self-contained. In real life... most enterprise codebases aren't nearly that clean. They're thousands of interconnected parts tied to old architecture, buried business logic, and Steve’s 2015 workaround still holding things together. It needs to adapt to: ☑️ Custom frameworks ☑️ Undocumented conventions ☑️ And yes, Steve's 2015 workaround that still holds the system together even after he's left So where should organizations actually focus? 1️⃣ End-to-end SDLC--> Don’t stop at coding. Deploy AI across requirements, design, testing, code review, CI/CD, and release management 2️⃣ Legacy code understanding--> Help developers navigate the messy reality of decades-old codebases. Make understanding the code accesssible (e.g. Crowdbotics) 3️⃣ Impact analysis & data flow--> Use AI to model how changes ripple across systems before deploying to prevent surprises (e.g. Cloudera's Octopai) 4️⃣ Institutional knowledge capture--> Train LLMs on your debugging history, architectural decisions, and runbooks. Context lives in Slack threads and old Confluence pages 5️⃣ Living documentation--> Auto-generate and maintain architecture diagrams, API specs, and SOPs as code evolves. Documentation shouldn’t lag 6️⃣ Security context by design--> AI should be constrained by your org’s secure coding standards so it doesn’t create vulnerabilities faster than you can patch them. The bigger picture? AI coding assistants aren’t the endgame. The real transformation comes from redesigning the entire lifecycle and giving AI the context it needs to operate in messy, real-world environments. Because in coding, context will always beat focus. Bain research: https://lnkd.in/eiGqnnzD Claude News: https://lnkd.in/ehCNpGme

I recently had the opportunity to work with a large financial services organization implementing OpenTelemetry across their distributed systems. The journey revealed some fascinating insights I wanted to share. When they first approached us, their observability strategy was fragmented – multiple monitoring tools, inconsistent instrumentation, and slow MTTR. Sound familiar? Their engineering teams were spending hours troubleshooting issues rather than building new features. They had plenty of data but struggled to extract meaningful insights. Here's what made their OpenTelemetry implementation particularly effective: 1️⃣ They started small but thought big. Rather than attempting a company-wide rollout, they began with one critical payment processing service, demonstrating value quickly before scaling. 2️⃣ They prioritized distributed tracing from day one. By focusing on end-to-end transaction flows, they gained visibility into previously hidden performance bottlenecks. One trace revealed a third-party API call causing sporadic 3-second delays. 3️⃣ They standardized on semantic conventions across teams. This seemingly small detail paid significant dividends. Consistent naming conventions for spans and attributes made correlating data substantially easier. 4️⃣ They integrated OpenTelemetry with Elasticsearch for powerful analytics. The ability to run complex queries across billions of spans helped identify patterns that would have otherwise gone unnoticed. The results? Mean time to detection dropped by 71%. Developer productivity increased as teams spent less time debugging and more time building. They could now confidently answer "what's happening in production right now?" Interestingly, their infrastructure costs decreased despite collecting more telemetry data. The unified approach eliminated redundant collection and storage systems. What impressed me most wasn't the technology itself, but how this organization approached the human elements of the implementation. They recognized that observability is as much about culture as it is about tools. Have you implemented OpenTelemetry in your organization? What unexpected challenges or benefits did you encounter? If you're still considering it, what's your biggest concern about making the transition? #OpenTelemetry #DistributedTracing #Observability #SiteReliabilityEngineering #DevOps

The significant improvement in agentic document extraction from 135 seconds to just 5 seconds, is not a simple case of hardware scaling. It reflects a fundamental Re-Architecture of the extraction stack into a multi-agent, memory-aware, and task-specialized system. The gain is primarily driven by four tightly-coupled advancements: 1. Decomposed Execution via Agent Orchestration: Rather than using a monolithic document pipeline that serially processes text, images, charts, and layout information, the system now deploys a central orchestrator that shards the document into specialized subtasks. Text blocks are streamed to lightweight text encoders; diagrams to image-captioning models; tables and forms to structured layout extractors. This decomposition enables parallel and intelligent routing—each agent only processes what it’s optimized for. 2. Shared Intermediate Representation (IR) and Zero-Copy Memory Access: The extraction process now leverages a normalized document layout graph (bounding boxes, hierarchy, semantic tags) as a shared IR across agents. This removes the need to serialize/deserialize intermediate results. With zero-copy in-memory sharing, multiple agents can work over the same document buffer simultaneously, bypassing costly I/O overheads and memory bottlenecks. 3. Optimized Execution with Partial Model Compilation and Quantization: Rather than invoking full transformer pipelines per agent, each model has been compiled (TorchScript or ONNX) with task-specific heads, quantized (int8 or dynamic), and loaded with only relevant weights. This significantly reduces inference time and memory footprint while preserving accuracy. 4. LLM-Ready Adaptive Structuring: The pipeline no longer outputs just raw elements—it converts extracted content into prompt-optimized structures using document schema registries, diagram-to-caption generators, and key-value form groupers. This reduces the cognitive load on downstream LLMs and makes the output plug-and-play. Looking ahead, we can expect even greater leaps. Multi-agent systems will become stateful, learning from prior document types and dynamically configuring agents at runtime. The orchestration will evolve to plan-inference-execution loops, allowing agents to "confer" over ambiguous sections. Models will likely shift to multi-modal sparse experts, activated selectively per region. Finally, expect on-device lightweight extractors that operate offline, with periodic sync back to cloud orchestration. It is not limited to performance gain, it’s the emergence of real-time document cognition systems, reshaping how software digests structured and semi-structured information.