Paul Adams

If you’re still running mission-critical ops on Word docs, spreadsheets, and tribal knowledge… you already know the punchline. Someone skips a step. A part goes missing. A test runs late. Nobody can trace why. And suddenly your “we’ll fix it later” process becomes the bottleneck that stalls a launch, slips a milestone, or triggers an audit you weren’t prepared for. Here’s the part most teams won’t say out loud: Your systems worked when you were 20 people. They quietly collapse when you’re 200. I keep hearing the same frustrations from Heads of Manufacturing, Mission Assurance, Propulsion Test leads, COOs: “We waste hours hunting for the latest procedure.” “Operators keep improvising because our docs are outdated.” “We found an error… but can’t trace where it originated.” “Quality is fine until it suddenly isn’t.” “We think we’re compliant… but we can’t prove it.” And nearly every team believes the same three things: “We can just build our own tool.” Until it takes 10x the time, 5x the cost, and still doesn’t integrate, scale, or get used. “Paper is fine.” But paper is the reason steps get skipped, data gets lost, and audits become nightmares. “We don’t have time to implement something better.” Translation: We’ll keep paying the cost of mistakes instead. Here’s the reality spoken from the front lines: The teams that scale the fastest aren’t the ones with the most people. They’re the ones with standardized, traceable, error-proofed operations. Manufacturing runs on rails. Testing becomes predictable. Operations stop firefighting. New hires ramp in days, not months. And leadership finally sees real-time truth, not guesswork. That’s why teams in space, energy, defense, robotics, and advanced manufacturing are moving to platforms built for complex operations, not duct-taped workflows. And yes, Epsilon3 is one of those platforms that helps them scale. Not because it’s flashy, but because: Setup is fast (2–3 short calls). Imports are automated (no manual rebuild). It works offline and in air-gapped environments. It tracks everything: parts, procedures, schedules, risks, CAPAs. FedRAMP High and enterprise security are non-negotiable. And it replaces a dozen tools you’re already frustrated with. If you’re scaling, hiring, prepping for a government audit, or staring at another avoidable delay… Ask yourself the real question: Can you afford to run next year on the same systems that nearly broke you this year? If the answer is no, let’s talk. I’ll show you how the teams who stopped tolerating operational chaos are now delivering on time, with fewer mistakes, and far less stress. Your future self and your technicians will thank you.

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𝗜𝗻𝘁𝗲𝗿𝗶𝗺 & 𝗡𝗼𝗻‐𝗣𝗜𝗩 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝗱𝗲 𝗘𝗮𝘀𝘆 𝘄𝗶𝘁𝗵 𝗔𝘅𝗶𝗮𝗱 𝗖𝗼𝗻𝗱𝘂𝗰𝘁𝗼𝗿 - 𝗙𝗲𝗱𝗥𝗔𝗠𝗣 𝗔𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗲𝗱 Federal agencies face long waits for PIV cards, fractured workflows for contractors, and ad‑hoc “band‑aid” MFA that exposes them to phishing risks.   Axiad Conductor (𝗙𝗲𝗱𝗥𝗔𝗠𝗣® 𝗔𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗲𝗱) delivers a turnkey, cloud‑native Authentication SaaS that: • 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗼𝗿𝘀 & 𝗖𝗿𝗲𝗱𝗲𝗻𝘁𝗶𝗮𝗹𝘀: PIV/CAC‑compatible Smart Cards, USB Keys, FIDO2 passkeys, certificates—mix & match for long‑ and short‑term access • 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝘀 𝘁𝗵𝗲 𝗙𝘂𝗹𝗹 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲: Issue, renew, expire, and revoke credentials on schedule—no manual processes or custom scripts • 𝗖𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: One control plane across Windows, macOS, Linux, and any existing IAM or IdP system • 𝗘𝗻𝗳𝗼𝗿𝗰𝗲𝘀 𝗣𝗵𝗶𝘀𝗵𝗶𝗻𝗴‐𝗥𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲: Every login workstation or application is protected with hardware‑backed authentication • 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝗥𝗮𝗽𝗶𝗱 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗚𝗮𝗶𝗻𝘀: Eliminate wait‑times for authenticators and reduce help‑desk tickets by up to 50% 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 • Pending PIV Issuance & Replacement • Derived Credentials • Project‑Based or Temporary Access • Contractors & Short‑Term Staff • Multi‑Device, Multi‑Credential Scenarios Streamline your interim authentication, shrink attack surfaces, and accelerate mission readiness. 👉 𝗟𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲: https://lnkd.in/ehvD8bvT #FedRAMP #ZeroTrust #IdentitySecurity #Authentication #AxiadConductor #FederalIT #Axiad #FIPS #PKI

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A question for vibe coders: How do you get an LLM that can can do a task for a single file to do it for many files? Say I have ~1000 source files, and I want to ask an LLM to validate something for *every* file Without getting into the specifics, what matters is that the task is simple enough that LLMs get it ~100% right for a single file. But if I give Cursor the root folder and ask it to do the task for every file in it, it gets lost. It starts reading random files, it validates a few, and then acts as if it validated all. Even when it creates TODO lists, it is far from exhaustive. Any advice? (The solution may be simple, but it's a frequent friction point for me.)

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Just wrapped up two days at the DPE Summit. Two years ago, when I gave the keynote, AI was barely on the agenda. This year, it was a headline, and AI has officially moved from the sidelines to the center of the developer productivity conversation. A few clear shifts stood out at the summit: - Inner loop acceleration. Coding agents are driving larger commits and faster throughput. The real challenge now is balancing this with quality, compliance, and security, shifting pressure to the outer loops. Expect background agents and automation to play a bigger role here. - Rethinking metrics. More code ≠ more productivity. The critical question is how to measure outcomes and isolate the ROI of AI-driven tools from everything else shaping team performance. The conversation is no longer “can AI help developers write code?” It’s now: how do we ensure AI translates into sustainable and measurable high-quality outcomes? That’s the next frontier for developer productivity.

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Security isn't a tech issue. It's economic. Last month, our OSV-Scanner Agent flagged 23 CVEs in our own dependency tree. The economic reality hit me like a freight train, one I did the math: we will be burning $4,200/month in developer hours manually triaging false positives. Here's the brutal math: Each CVE took 1 hour to verify reachability. Most were phantom threats - unused code paths in transitive dependencies. Our Researcher Agent now performs exploitability analysis in 180 seconds, feeding context directly to our patch workflow. Result: We cut CVE triage time by 89%. Our SOC2 prep timeline this summer compressed from 6 weeks to 2. But here's what surprised me most: The economic model flipped. Instead of security being a cost center, our automated patch generation actually accelerated feature velocity. When your CI runner auto-remediates vulnerabilities before they reach production, we stay in flow state. The broader implication? Most companies are solving the wrong problem. They're buying more scanning tools when they should be buying decision & patching engines. Every unresolved CVE carries compound interest—technical debt that doubles every sprint cycle. We're preparing for SOC2 and ISO27001 audits using XOR to audit XOR. The irony isn't lost on me: our own medicine tastes pretty good. What's your security budget really buying you—protection or process theater? #CyberSecurity #SOC2 #StartupLife

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Your agent has production access. Your intern doesn't. Think about that. We gate human access with background checks, training, and approval chains. AI agents? Deployed Friday. Full permissions Monday. No questions asked. The security gap isn't technical. It's philosophical. ━━━━━━━━━━━━━━━━━━━━━━ 🔴 𝗧𝗛𝗘 𝗕𝗥𝗨𝗧𝗔𝗟 𝗧𝗥𝗨𝗧𝗛: 95% of AI pilots stall before production. Not because the tech fails. Because governance doesn't exist. One agent with write access to your CRM. One hallucination. One mass email to your entire customer list. That's not a hypothetical. That's a Tuesday. ━━━━━━━━━━━━━━━━━━━━━━ Agents need the same rigor as employees: → Onboarding (capability testing before production) → Least privilege (read-only until proven) → Monitoring (every action logged) → Reviews (quarterly permission audits) → Offboarding (credential rotation when deprecated) Your agent lifecycle should mirror your employee lifecycle. Anything less is a career-ending incident report. Does your agent have more access than your newest hire? __________ ♻️ Repost if your agent governance needs a reality check ➕ Follow Mohammad Syed for AI & Cybersecurity insights

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This is a really interesting article about writing agents with the Claude Agent SDK. It gets into design advice that you should consider when writing an agent with Claude. It chimes with what I’ve read about the development of Claude Code (write as little business logic as possible and concentrate on getting out of the way of the model). The general takeaway for me is from the three-block flow chart in the middle: Give the agent good tools/affordances in three categories: Gather Context, Take Action, Verify work. Do that at the right granularity, with good feedback, and then let the model do the driving. It’s deceptively simple, but it has a goldilocks quality to it. Trying to make a more complex flow chart with many more boxes and lines constrains the AI to just the things you could think of in advance and makes it overfit to what you know today. Trying to make it simpler by, for example, giving the agent a whole existing API as a single tool can overwhelm it with too much choice and then too much context in the responses. It’s definitely worth reading if you're interested in building or using agents, irrespective of AI vendor. https://lnkd.in/eKF2rXWb

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When credentials are compromised, the clock starts ticking. Our team at Plurilock developed this playbook based on direct field experience, technical response workflows, and the common gaps we see in most organizations' ATO readiness. This is the most actionable and practical ATO incident response guide you’ll find. It is built specifically for CISOs and IT leaders who need to act fast and get it right the first time. Inside the checklist:   ✅ Contain and isolate compromised accounts ✅ Revoke tokens and enforce session logout ✅ Launch triage, recovery, and investigation protocols ✅ Communicate with stakeholders clearly and quickly If you are responsible for minimizing the impact of account takeovers, this guide is for you. Inbox me and I’ll send you the PDF. 📨

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See the fastest LLM serving engine, 100% open source!!! If you’re running multi-round QA, RAG, or any long-context workload, you know the pain: slow time-to-first-token and expensive bills. You should take a look LMCache, a next-gen LLM serving engine extension designed to slash TTFT (time to first token) by 3–10x and boost throughput. How? LMCache stores KV caches from any reusable text (not just prefixes) across GPU, CPU DRAM, and disk, then shares them across serving instances. That means fewer recomputations, faster responses, and huge GPU savings. Key highlights: 🔥 Seamless integration with vLLM v1 ⚡ High-performance CPU KV offloading 🔗 P2P KVCache sharing across servers 💾 Storage on CPU, Disk, or NIXL ✅ Supported in vLLM production stack, llm-d, and KServe 📦 One-liner install: pip install lmcache If you’re scaling LLM inference, you should check it! 👉 GitHub link in the comments. #LLM #AI #Inference #OpenSource #RAG #AgenticAI

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"Now that code is free..." Any assertion that follows this is predicated on a falsehood. Plausible-looking code might now be cheap. But *working*, reliable code that can be changed at low cost without breaking it? For most teams, that's even more expensive now.

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While everyone was talking about Opus 4.6, for me the true killer feature of the recent Claude Code updates is the agent teams. It's been something i've been trying to achieve with customization for a while. Custom agents, orchestration scripts, specific CLAUDE.md instructions to coordinate work... with some degree of success. But what Anthropic shipped natively is a WHOLE different level. What makes this stand out is the inter-agent communication. We're not talking about simple fan-out/fan-in where you spawn workers and collect results. These agents talk to each other. Peer-to-peer messaging, dependency-aware task graphs that auto-unblock, agents that self-claim work from a shared task list. The lead can even enter Delegate Mode where it does ZERO implementation, only coordination. The image below is from one of my setups. A team manager orchestrating a librarian agent, a PhD lead, and 5 research sub-tasks with blocking dependencies. The librarian unblocks the research tasks, the PhD lead aggregates everything. All coordinated autonomously. And with this a whole new world of orchestration just unveiled itself. Distributing work across agents is the "easy" part. You break down tasks, assign owners, define dependencies. The HARD part, and what MOST stands out now, is aggregation. How do you take the output of 5 parallel agents, each with their own context window, and synthesize it into something coherent? That's the new skill. Anthropic themselves used 16 parallel agents to build a 100,000 line Rust C compiler that compiles the Linux 6.9 kernel. No human actively coding. ~$20,000 in API costs over ~2,000 sessions. We went from pair-programming with AI to managing AI engineering teams. The skills that transfer are the ones from engineering management: task decomposition, context management, knowing when to intervene vs let the team self-organize. This is a new paradigm, and i think it opens up several possibilities we haven't fully explored yet. ref.: https://lnkd.in/dVCe344z

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Tired of hearing excuses from your dev team? (Here’s what high-performing teams do differently.) Every time you ask why a project isn’t done, there’s a new story: ⤷ “We ran into unexpected challenges.” ⤷ “The requirements changed.” ⤷ “It turned out bigger than we thought.” It’s like playing whack-a-mole with your own roadmap. But here’s the truth: Good dev teams don’t hide behind blockers. They build with boundaries. What the Best Teams Do: 1/ Set unmovable deadlines → Not vague estimates → Real dates that drive accountability 2/ Define scope clearly → What’s in? What’s not? Put it in writing → Scope creep dies in documentation 3/ Protect momentum → No shifting goalposts → No sprinting in circles ⤷ Why It Matters: A dev team that avoids hard lines doesn’t just delay launches. They build a wall between you and your goals. If that’s what you’re up against, it’s not a people problem, is a standards one. Ready to work with a team that delivers? Start here: https://lnkd.in/gBr3wFRC 👉 Follow for more no-BS advice on building faster, accountable dev teams.

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