Thinslices

Transitioning to a more backend-focused role within the same project at BMJ, I am now spearheading the development and implementation of APIs using Node.js. This significant project revolves around parsing HTML through these APIs, enhancing the accessibility and delivery of article content.

✅ Streamlined Content Delivery: By implementing APIs using Node.js, I am enhancing the way we handle and present data. This has led to an improved method of parsing HTML, enabling a more efficient delivery and presentation of article content.

✅ Expanded Backend Capabilities: Taking on a backend-focused role, I am deepening my expertise and versatility as a Full Stack Web Developer, ensuring that I stay adaptable and proficient in all aspects of development, from frontend to backend.

✅ Enhanced Project Understanding: By working on different aspects of the same project, I have gained a more holistic understanding of the project's architecture, which contributes to my ability to deliver efficient and effective solutions.

✅ Architectural Design and Leadership: I've taken the helm in designing the architecture of a separate internal project, focusing on creating a scalable and maintainable long-term solution. My role involves guiding and overseeing the work of two developers, providing mentorship and support as needed.

✅ Collaborative Communication: My responsibilities extend to constant communication with business stakeholders and UI/UX designers. This collaboration is crucial in aligning project requirements and ensuring that we deliver the best software solutions that meet business needs and enhance user experience.

This new endeavor signifies my constant pursuit of growth and learning within the realm of web development, reinforcing my commitment to delivering top-notch solutions. The experience of working on such a diverse range of tasks within the same project broadens my perspective and reinforces my adaptability in the fast-paced world of web development.

Software Developer

As a Frontend Developer at BMJ (British Medical Journals), a prestigious UK-based organization, I contribute to the development and maintenance of the user interface for web applications. My role involves a strong focus on improving web performance, enhancing accessibility, and streamlining deployment processes. My key achievements include:

✅ 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐞𝐝 𝐚 𝐬𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐰𝐞𝐛 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 by migrating BMJ's web applications to Next.js. This strategic decision resulted in an increase in speed, enhanced user experience, and an impressive Google Lighthouse average score of 90%.

✅ 𝐀𝐭𝐭𝐚𝐢𝐧𝐞𝐝 𝐮𝐧𝐢𝐯𝐞𝐫𝐬𝐚𝐥 𝐚𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬 by adhering to WCAG 2.1 guidelines during the development process. This adherence resulted in more inclusive and accessible web content for all users, reinforcing BMJ's commitment to providing accessible content to the global medical community.

✅ 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐞𝐝 𝐚𝐧𝐝 𝐞𝐱𝐩𝐞𝐝𝐢𝐭𝐞𝐝 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 by implementing a fully automated CI/CD pipeline powered by GitHub Actions. This automation facilitated seamless QA and enabled multiple production releases of any or all BMJ Journal sites multiple times a day, ensuring timely updates to crucial medical information.

Frontend Developer

As a Web Application Developer, my role was centered around developing robust web applications using JavaScript and React.js. I collaborated effectively with team members, contributed to code reviews, and engaged in testing processes. Here are my key achievements:

✅ 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐞𝐝 𝐩𝐫𝐨𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐢𝐧 𝐚𝐝𝐚𝐩𝐭𝐢𝐧𝐠 𝐭𝐨 𝐧𝐞𝐰 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐚𝐧𝐝 𝐜𝐨𝐝𝐢𝐧𝐠 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 by demonstrating a strong aptitude for learning and evolving my approach. This led to 𝐬𝐨𝐥𝐢𝐝𝐢𝐟𝐲𝐢𝐧𝐠 𝐦𝐲 𝐬𝐤𝐢𝐥𝐥𝐬𝐞𝐭 𝐚𝐧𝐝 𝐞𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 𝐭𝐡𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐨𝐟 𝐜𝐨𝐝𝐞 𝐈 𝐩𝐫𝐨𝐝𝐮𝐜𝐞𝐝.

✅ 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐜𝐨𝐝𝐞 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐭𝐞𝐚𝐦 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 with a keen eye for detail by 𝐥𝐞𝐯𝐞𝐫𝐚𝐠𝐢𝐧𝐠 𝐆𝐢𝐭 𝐟𝐨𝐫 𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐚𝐧𝐝 𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐩𝐚𝐫𝐭𝐢𝐜𝐢𝐩𝐚𝐭𝐢𝐧𝐠 𝐢𝐧 𝐭𝐞𝐚𝐦 𝐦𝐞𝐞𝐭𝐢𝐧𝐠𝐬. This contributed to 𝐚𝐝𝐡𝐞𝐫𝐢𝐧𝐠 𝐭𝐨 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐛𝐞𝐬𝐭 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬, 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭𝐥𝐲 𝐦𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐜𝐨𝐝𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐬, 𝐚𝐧𝐝 𝐚𝐝𝐝𝐢𝐧𝐠 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐢𝐧 𝐭𝐞𝐚𝐦 𝐝𝐢𝐬𝐜𝐮𝐬𝐬𝐢𝐨𝐧𝐬.

Web Application Developer

Remote Work

As a Frontend Developer at an innovative startup, my mission involved contributing to the transformation of the freelancing industry. My role was pivotal in the creation of a digital platform aimed at redefining the experience for freelancers and businesses, similar to Upwork.

✅ 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐞𝐝 𝐚 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 by utilizing my skills in React.js and TypeScript for frontend development, which 𝐥𝐞𝐝 𝐭𝐨 𝐚 𝐬𝐦𝐨𝐨𝐭𝐡 𝐚𝐧𝐝 𝐢𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞 𝐮𝐬𝐞𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞.

✅ 𝐏𝐫𝐨𝐦𝐨𝐭𝐞𝐝 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 by contributing to the robust backend of our platform using Nest.js, which 𝐞𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐬𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐨 𝐦𝐚𝐧𝐚𝐠𝐞 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞𝐝 𝐮𝐬𝐞𝐫 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬.

✅ 𝐌𝐚𝐢𝐧𝐭𝐚𝐢𝐧𝐞𝐝 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐬𝐬𝐮𝐫𝐚𝐧𝐜𝐞 by employing Cypress for end-to-end testing, which 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐞𝐝 𝐛𝐨𝐭𝐡 𝐢𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐨𝐯𝐞𝐫𝐚𝐥𝐥 𝐬𝐲𝐬𝐭𝐞𝐦 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲, 𝐞𝐧𝐬𝐮𝐫𝐢𝐧𝐠 𝐚 𝐡𝐢𝐠𝐡-𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐨𝐮𝐭𝐩𝐮𝐭.

The experience, though short-lived, was enriching, providing deep insights into the dynamism and rapid innovation cycles characteristic of startup environments. This opportunity reinforced my ability to adapt quickly to new technologies and methodologies while delivering high-quality solutions.

🚀 Elevate Your Digital Presence | 🔥 Skilled React Frontend Developer: Crafting Exceptional UIs for Growth-Driven Businesses | Advocate of Deep Work & Mindset for Success

As a 𝗥𝗲𝗮𝗰𝘁 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 with 𝟯 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲, I channel my passion and skills to assist businesses worldwide by designing, implementing, and optimizing their web applications.

Imagine a digital ecosystem where your web applications not only captivate users but also guide them towards your desired goals, effortlessly. That's my mission.

𝗠𝘆 𝗦𝗸𝗶𝗹𝗹𝘀:

✅ 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗪𝗲𝗯 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: • JavaScript, React & Next.js for dynamic and responsive web apps • CSS & TailwindCSS for crafting visually pleasing interfaces • Storybook for efficient UI component development and testing • TypeScript for ensuring type safety and error reduction • Node.js, Python & Django for sturdy backend architectures

✅ 𝗨𝘀𝗲𝗿-𝗖𝗲𝗻𝘁𝗿𝗶𝗰 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀: • Intuitive designs to maximize user engagement • Seamless interfaces to increase productivity without overwhelming users

✅ 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽𝘀: • Building strong partnerships to understand and fulfill unique business needs • Iterative development to continuously integrate feedback and optimize results

I've had the privilege of working with a diverse range of clients, assisting them in enhancing their digital presence and confidently achieving their goals.

I am currently focusing on my existing projects and not actively seeking new opportunities. However, I am open to remote collaborations that allow me to extend my expertise to your projects, irrespective of geographical boundaries.

Let’s collaborate to transform your app experience into something truly remarkable! Feel free to reach out to see how my skills can serve your business.

📱Tel: 0749641796 ✉️Email: florin.dobinciuc.dev@gmail.com

Florin Dobinciuc

AI Flow

• Working with small and medium companies, building deep tech AI products from 0, extending existing projects, and taking 100% ownership of the technical part, so you can focus on your business. 

• Having a deep understanding of AI and Machine Learning, I also do consulting for high-growth companies. Working with Python, Typescript and C++.

• Clients include BP, ExoMatter.ai, colayer.io, deinestudienfinanzierung.de and Interflexion.

Founder, Lead ML Engineer & AI Consultant

Metaphysic.ai

• In charge of the ML team. My main goal is to productionize research projects and lead the team towards longer-term goals.

Lead Machine Learning Engineer

• Working on generative AI for hyperrealistic video content creation. Building machine learning models, pipelines, and infrastructure for training and inference.

• Reduced the existing ML pipeline time and cost by 45%.

Senior Machine Learning Engineer

Uplift.ltd

• Worked on AI, Cloud,  Data Engineering, and Full Stack projects. Handling the whole product lifecycle, including planning, implementation, and deployment.

• Clients include interflexion.com, collectoralerts.com, and perfectlypitched.co. I worked with them to build features in Node.js, Django, Next.js, and React, and scaled their infrastructure on Google Cloud. Built apps from scratch with Next.js - GraphQL}- PostgreSQL, deployed on Digital Ocean.

Senior Software Engineer

Google

• Did research on feature selection and built a tool that makes Machine Learning iterations up to 70% faster. Clients included Ads and Cloud.

AI Engineer | Python, PyTorch & Tensorflow

• Improved the user experience in Search and Maps, by building data analysis tools for Geo Location. Used C++, MapReduce, and SQL to build a distributed data pipeline for analyzing and fixing locations with issues.

Software Engineer

Bloomberg LP

• Built a sharing feature in the internal collaboration tool with 200K daily users. Worked Full-Stack with JavaScript and TypeScript, leading the project from start to delivery.

• While in the News Feeds department I moved existing services to Docker, making development flows smoother, and impacting more than 300 engineers. I collaborated with stakeholders, identified their needs, and implemented the migration.

• Built a cache infrastructure for mapping users to communities that currently serves 200 requests/second, significantly improving the loading time client-side. Used C++ and Python for development, Redis for building the cache layer and RabbitMQ for listening to database updates. Collaborated with the Sharing Framework to implement and integrate the cache infrastructure with their existing system.

Software Engineer Intern

METRO AG

Bosch

• Worked on anomaly detection in videos containing thermal events. Starting from unlabeled videos of running circuit boards, built a classification model which decided, for each frame, whether or not it had an anomaly (contained fire or smoke). Used Keras for building the Convolutional Neural Network.

Data Science Intern

Bitdefender

• Worked on a data structure library in C (hash tables, binary search
trees, heaps, linked lists, and arrays).

Innovation Labs Romania

• Built an Android application for electric scooter sharing. My team was among the best ones which granted us a mentorship program to further develop our product.

Innovation Labs Hackathon 2019

Senior Machine Learning & Full Stack Engineer • 9+ years in AI & ML • I built AI at Google, Bloomberg, and early-stage startups • Founder @AI Flow | We build end-to-end data products • AI Consultant • Open to B2B, remote projects.

 • I've been in the software industry for more than 8 years, splitting between Software Engineering and AI. After spending some years at Google and Bloomberg solving problems at scale, and building products for startups, I decided to build AI Flow, an AI focused Software Agency. We work with small and medium companies, building deep tech AI products from 0, extending existing projects, and taking 100% ownership of the technical part, so you can focus on your business. Having a deep understanding in AI and Machine Learning, I also do consulting for high growth companies. Let's get in touch!

 • In my day-to-day work, I am:
☑️ Designing and prototyping large-scale ML &  data systems.
☑️ Writing clean Python, C++, and Typescript.
☑️ Leading and managing high-performing teams.
☑️ Communicating clearly and often.
☑️ Building and training models with Pytorch.
☑️ Developing full stack with NextJS, Node, React, and Django.
☑️ Deploying on any cloud with Docker and Kubernetes.
☑️ Focusing on quality, simplicity, and a great UX.
☑️ ... and many more.

 • Only interested in remote contract opportunities •  Book a meeting on https://antonmih.ai/meet

 • Website: antonmih.ai • Phone: ‭+40 721 658 931 •  Mail: mihai@aiflow.ltd

Mihai Anton

Mic check done. Seats are getting filled. Looking forward to see how the surprise I prepared for the audience lands, but I’ll share more about it after. 

TEDxBrașov, excited for this!

(8) Feed | LinkedIn

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li_at

Talks about #ai, #startups, #remotework, #freelancing, and #softwareengineering

Founder & Lead ML Engineer @ AI Flow | Ex-Google & Bloomberg | Building Agentic AI & ML systems that scale | 10 years in AI & ML

Off to Dubai for the next couple of days. Catch me at the World Tech Summit! 

If you’re around and into AI, engineering, or just good coffee + tech talk, let’s meet. I’d love to connect with fellow builders, founders, or anyone curious about GenAI beyond the hype.

So, let’s grab a coffee and talk about AI. DMs are open.

Travel can be a powerful add-on to your work, as long as you have the right systems built for it.

Switching environments (without dropping structure) helps me reset my thinking, notice weak spots, and come back to problems with a sharper eye.

This week, I’m balancing work with attending the World Tech Summit. That means:

- Deep work blocks stay protected (calendar is the source of truth).
- One problem per block, written in one line before I start.
- Async routines keep moving in the background.

New scenery, same structure, sharper focus.

Three months in Cluj to turn “I’m testing an idea” into “people are using my product.”

I wish I had this program when I was 20.

If you’re under 25 and already building in your spare time, skip the CV part and come test your ideas properly. You’ll get time (a monthly allowance), room to experiment (a real ops budget), and a real prize on the line (an investment from @ Lost Astronaut VC).

It’s supported by @Cluj Startups, so you’re not just in a program, but in a community. Their office, Founders Hive, is your home base during the program.

If you join, tag me. I want to see what you build!
Apply here: thebasejump.com/arena

Some of the best decisions I’ve made came right after I paused.

Not when I pushed harder, or when I tried to force a breakthrough. But when I stepped away for a few minutes, cleared the noise, and let the problem breathe.

In tech, it’s easy to mistake urgency for progress, as it might seem like you’re always one sprint behind, one release away, one Slack message from making the next move. But in architecture, product design, or hiring, rushed decisions have a long half-life.

It’s rarely the loudest thought that’s the right one. Most often, it’s the one that still makes sense after a walk.

I see a lot of AI startups treat architecture like something you "fix later." The mentality is: ship the MVP, get traction, then rebuild properly.

Here's what actually happens:

Your MVP works for 50 users. You get traction. Now you need to scale to 500, then 5,000. But your system was built for a demo, not for growth.

So you're rewriting core infrastructure while trying to keep the lights on, which means you're patching instead of scaling. And every new feature takes twice as long because the foundation is shaky.

The time you "saved" by skipping architecture? You're paying it back with interest.

I'm not saying over-engineer, but I encourage you to think about these questions first:

- How will this handle 10x the load?
- Where will the bottlenecks show up?
- Can we monitor what matters?
- Is this decision reversible, or are we locked in?

The best teams I've worked with move fast AND build clean. They know which corners are safe to cut and which ones will cost them later.

Who is asking to “add GenAI,” and how you can shape the conversation:

A) Tech lead

Ask: What bottleneck do you want to eliminate next month?

If they can show a metric, design a scoped assist. If not, run a one-week discovery.

B) Founder

Ask: What outcome are we missing, and what would prove it?

If they can pick one metric, design a pilot. If not, map the value chain and choose the highest-friction job.

C) The “We need AI” stakeholder

Ask: Which workflow, and what value per run?

If they cannot answer, pause the build and run a process audit first.

Always remember that tool choices come last, while clarity (on job, outcome, and metric) comes first.

OpenAI is aiming for an “intern-level research assistant” by 2026 and a “legitimate AI researcher” by 2028. It’s a big milestone, sure, but I believe the most interesting part is beyond the headline. 

Let’s dig deeper:

- “Intern by 2026” reads to me like: a model that can read papers, compare findings, and suggest next steps. This implies it can do useful analyst work, still supervised.

- “Researcher by 2028” suggests autonomous experiment loops with human oversight. This could be similar to a colleague you manage, not just a tool you poke. If they land anywhere near this, the leverage will shift from prompt cleverness to problem framing, data plumbing, and evaluation discipline.

What I recommend to anyone serious about agents to do:

- Treat every agent step as a hypothesis. Add validators, timeouts, and rollbacks so progress is earned, not assumed.

- Build evals early. Keep a living regression suite with clear failure buckets (fabrication, tool misuse, spec drift).

- Get provenance in order. Lineage, permissions, and auditability beat “move fast” when an autonomous loop makes a wrong turn.


This way, you’re already designing for trust. When capabilities unlock, your system is ready to absorb them without chaos. 

Guardrails, evals, and data lineage aren’t nice-to-haves anymore. They’re how you convert tomorrow’s “AI researcher” into reliable throughput today.

Too many AI projects still start with “Which model should we use?”, instead of:

- What’s the real problem we’re solving?
- What decision needs to be improved?
- Where does it fit in the system?

This results in a parade of flashy demos that never ship, because there was no strategy to begin with.

LLMs are amazing tools, but they’re not strategy. 
They don’t know your business. You do. Start there.

There’s real gold hiding in failed sessions. Your job is to replay, map, and fix the first mile.

Innovation doesn’t knock on your door. It walks in while you’re still fixing last year’s backlog.

Most companies aren’t disrupted by what’s new. They’re caught off guard by what they left unattended too long: technical debt, outdated infra, brittle systems, or data they never bothered to clean.

What you ignore today becomes tomorrow’s blocker. Or worse: your competitor’s leverage.

We spend a lot of time discussing model accuracy and deployment speed, which are crucial. But one of the most underrated technical decisions, and a critical safeguard for sustainable AI, is designing for the "Human-in-the-Loop." 

This isn't a sign of AI weakness; it's a strategic strength.

Humans bring judgment, contextual understanding, and ethical oversight that algorithms simply can't replicate, especially in ambiguous or high-stakes scenarios. 

Whether it's validating model predictions, handling edge cases, or providing feedback for continuous learning, integrating human expertise at key decision points enhances accuracy, mitigates bias, and builds trust. 

For any Mid-sized company with Data looking to formalize AI, or an AI Startup building mission-critical products, prioritizing this interaction ensures your AI remains reliable and accountable.

Agents don’t fail at “intelligence”, they fail at handoffs. 

Who are the usual suspects?

- Missing context → the next step is a guess.
- Ambiguous ownership → nobody rescues stuck work.
- Silent failures → confidence drops and no one knows.

Here’s how you can design around it: one job per agent → explicit states → confidence thresholds → human gates on high-impact steps → clear recovery.

Then, make sure to track and learn from the insights. Start by looking at four numbers: handoff success rate, time to first correct handoff, intervention rate, and silent failures (target: zero). If those improve, you’re on the right track. 

Your model might not be bigger, but it surely has cleaner edges.

Another release won’t help if no one owns the outcome. Pick one owner, pick one number.

Make ownership visible in how the work runs. 

Here’s the checklist I use to wire it in:

- One owner per workflow. One name, one inbox.
- One number that fits the goal.
- Ship to usage. “Launched ✅” doesn’t count until people rely on it.
- On-call = accountability. If you build it, you take the call when it breaks.
- Weekly user time. 30 minutes with real users > 30 comments in a doc.
- Rollback planned upfront. Know how to back out before you push.
- Monthly teardown. What worked, what hurt, what we stop doing.

When ownership is clear, priorities stop drifting and feedback loops stay alive.

What actually moved the needle this year? What quietly got in the way?

I put together 4 paste-only prompts you can run in any AI chat. All you need = your notes as inputs. The answers will be honest, and after running all 4 of them, you should also have a plan for your next 12 weeks. 

Treat December as the cleanup month: spot inconsistencies, clear the friction. This way, you can start 2026 with the insights you need.

Save this carousel for a quiet hour. 

I’ll come back with a version for businesses soon.

Don’t scale the unknowns

If one user run feels fuzzy, a thousand will amplify the mess. That’s the right moment to slow down and trace the path: inputs → decision rules → next action → owner → evidence. 

Make the edges crisp at 1×, then turn the dial.

“We want to use our internal data for AI.”

Cool. But... why?

One mistake I see over and over: teams don’t define success metrics before they start. It’s treated like you can throw your Google Drive into a magic AI bin and expect results.

AI isn’t magic. It’s a tool. And tools need a purpose. So, are you trying to:

- save time on a workflow?
- cut operational costs?
- lower response times for your support team?
- improve the quality of content or insights?

Or is your product itself AI‑driven, and leveraging internal data is the only way to serve your clients?

Without a clear “why,” you’re just experimenting in the dark, and it shows when you try to measure impact later.

Before you start building, get specific about what “better” means for you. Your data (and your team) will thank you.

What success metrics do you look at before kicking off an AI project?

Reliable agents come from clear lifecycles, tight scopes, confidence gates, and built-in observability.

“Agents” look great in a demo. Then they meet real work: partial context, fuzzy ownership, and failures that don’t announce themselves. What helps is treating agents like operators in a system, not like magic.

What actually breaks

- Missing state. Acting without the latest facts or forgetting what matters.
- Ambiguous ownership. Nobody is sure who approves, who fixes, who decides next.
- Silent errors. Things go wrong quietly and, the customers find out first.

How to make them dependable

- Name the lifecycle. Write the states down (queued → in_progress → needs_human → done → audited) and stick to them.
- One job per agent. Clear intent, clear tools, clear output.
- Confidence gates. When certainty dips, pause and escalate.
- Human checkpoints for high-risk moves. Review before actions that are costly or hard to reverse.
- Build in observability. Log inputs, tools used, decisions, and the evidence behind them.

A quick readiness test I use:

- What is this agent allowed to do, when, and with which proof?

If that isn’t crisp, shipping will just move the ambiguity into production.

Are you building for the next two years or the next two weeks?

Most teams say long-term, but their roadmaps disagree. If a routine model upgrade, a traffic spike, or a vendor price change knocks things off course, you’re just renting stability. 

I always encourage teams to make three decisions early on:

- First, what must be resilient: identity, data contracts, evaluation harness, and billing. Define SLOs, error budgets, RTO, and RPO. Be explicit about how each part fails and how it recovers.

- Second, what should stay swappable: model provider, embeddings, vector store, feature flags, analytics SDKs. Create clean seams, hide them behind interfaces, and keep a tested fallback that preserves core function even at lower quality.

- Third, what you will outsource: queues, authentication, and observability. They are essential, but they rarely set you apart. Spend engineering time on the paths that create user-visible value.

Which of these three decisions have you postponed? What would break first as a result?

Personalization isn't a model problem. It’s a data problem.

The model can only work with what you feed it. And most teams feed it scraps:

- Public datasets
- Generic CRM data
- Surface-level attributes

And then they wonder why their results look like everyone else’s.

The teams winning the hyper-personalization are:

- Investing in collecting meaningful signals
- Labeling their data for depth, not just volume
- Building internal pipelines, not just prompt wrappers

You don’t need a bigger model. You need better input.

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