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

Founder & Lead ML Engineer @ AI Flow | Ex-Google & Bloomberg | Building AI Agents that scale | 10 years in AI & ML | Building AI Flow, an AI & ML focused consultancy that designs, builds, and scales end-to-end AI products for startups and mid-market companies.


> What we do at AI Flow

- AI consulting - understand how to make most of your data and benefit from the Agentic AI wave.
- AI Strategy & Roadmapping - cut through hype, map the fastest path to ROI
- Custom ML & Gen-AI systems - from data pipelines to fine-tuned LLMs and Agents
- Data platforms - ETL, ingestion pipelines, document understanding end to end solutions
- Embedded AI Teams - senior engineers who plug in and deliver from day 1

> Proof over promises

- 45% pipeline cost & time drop for hyper-realistic video at Metaphysic
- 65% faster ML iterations for Google internal ML teams
- Millions saved automating invoice processing for a top-5 Oil & Gas firm
- Built tools used by 200K daily users, with more than 200 request/second at Bloomberg.
- 85% RAG & LLM pipeline cost drop through data cleaning pipelines for law companies.

> Why clients trust us

“I trusted you to deliver a good result, and that’s exactly what happened.”
- Josua Vieten, CEO @ ExoMatter

“Deep technical expertise across the stack. Mihai understands business context and proposes scalable solutions.”
- John Hack, CPO @ Interflexion

“Mihai makes it easy to get projects done. He is pragmatic, ambitious, and takes initiative.”
Paul Craciunoiu, Founder @ Uplift


> Let’s talk about building the AI future

Open to B2B, remote projects. If you need a sharp, senior team to ship real AI, book a 30-min call: antonmih.ai/meet

Mihai Anton

You wouldn't hire a mechanical engineer who only used AI-generated blueprints.

You wouldn't trust a doctor who only used ChatGPT for diagnosis.

Yet many companies hire "AI engineers" who've never debugged a production system.

Tools change. Fundamentals don't.

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Watch it here: https://www.youtube.com/watch?v=CLuv-X2Fm_s

Machine Learning by Stanford University on Coursera

Cambridge English Advanced English

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

Founder & Lead ML Engineer @ AI Flow | Ex-Google & Bloomberg | Building AI systems that scale | 10 years in AI & ML | Building AI Flow, an AI & ML focused consultancy that designs, builds, and scales end-to-end AI products for startups and mid-market companies.


> What we do at AI Flow

- AI consulting - understand how to make most of your data and benefit from the AI wave.
- AI Strategy & Roadmapping - cut through hype, map the fastest path to ROI
- Custom ML & Gen-AI systems - from data pipelines to fine-tuned LLMs
- Data platforms - ETL, ingestion pipelines, document understanding end to end solutions
- Embedded AI Teams - senior engineers who plug in and deliver from day 1

> Proof over promises

- 45% pipeline cost & time drop for hyper-realistic video at Metaphysic
- 65% faster ML iterations for Google internal ML teams
- Millions saved automating invoice processing for a top-5 Oil & Gas firm
- Built tools used by 200K daily users, with more than 200 request/second at Bloomberg.
- 85% RAG & LLM pipeline cost drop through data cleaning pipelines for law companies.

> Why clients trust us

“I trusted you to deliver a good result, and that’s exactly what happened.”
- Josua Vieten, CEO @ ExoMatter

“Deep technical expertise across the stack. Mihai understands business context and proposes scalable solutions.”
- John Hack, CPO @ Interflexion

“Mihai makes it easy to get projects done. He is pragmatic, ambitious, and takes initiative.”
Paul Craciunoiu, Founder @ Uplift


> Let’s talk about building the AI future

Open to B2B, remote projects. If you need a sharp, senior team to ship real AI, book a 30-min call: antonmih.ai/meet

AI Flow | We build AI powered products | AI, Machine Learning, and Data managed services

AI Flow

Senior Software Engineer | AI consultant | Working in AI before it was cool

Resume

Created a social network for frequent travelers, where they can post experiences, see what others explore, and follow their steps. Take a look to see how it works!

IdeaList

MihaiAnton has 14 repositories available. Follow their code on GitHub.

MihaiAnton - GitHub

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

AI Flow #2 is up.

This week: how we helped a client cut spending from $10K/month to $2K/month, without touching the model.

After analyzing the situation, we found the problem was the input.

They were feeding entire HTML pages to the LLM (cookie banners, navigation menus, scripts, SEO content). The model was drowning in noise.

The fix was simpler than they expected. I break down what we did and the results in this week's edition.

Every time a new model drops, I get the same wave of messages from clients: should we rebuild? Should we switch? Are we falling behind?

This week it's GPT-5.4. Next month it'll be something else.

The honest answer is always the same: it depends on what you're optimizing for, and most teams haven't written that down anywhere.

- If cost is the constraint and the new model delivers similar quality for less, run the eval and test it.
- If accuracy drives revenue or directly reduces support volume, measure whether the delta is real enough to justify the migration work.
- If you're upgrading because it's newer and that feels like the right move, that's the one reason I'd push back on.

The things that determine whether your system works didn't change this week. Your data quality didn't change. Your evaluation criteria didn't change. The problem your users need solved didn't change.

A better model sitting on top of a broken pipeline is still a broken pipeline. Test it against the metrics you already track. If they move, ship it. If they don't, look at other areas that actually move them, like the data sources feeding the system.

AI Flow #3 is up.

This week: a client came in ready to invest in AI. Budget approved, timeline set.
We told them: not yet.

After analyzing their setup, we found data in 6 different systems. No tagging, no structure, half of it contradicting the other half.

Building on that foundation would have guaranteed a broken system. So, we made a different plan instead.

"How fast can we see ROI from AI?"

Depends on what you're measuring.


- Time savings? Weeks. We automated proposal generation for a construction company. From 4 hours per proposal to 20 minutes, they felt the ROI immediately.

- Revenue increase? Months. You need time to test, iterate, and prove the feature changes behavior.

- Cost reduction? Somewhere in between.


Most companies expect all three at once, immediately.

My advice: focus on time savings first, as it’s fastest to prove.

"Should we rebuild for the new model?"

The problem isn't the question itself. It's what comes next, as most teams usually ask, "Is it better?" instead of "Is it better for what we need?"

The answer depends on what you're optimizing for. Here are 3 examples.

Should we use the latest model?

2026 isn't about chasing what's new. 

It's about building systems that work regardless of which model is trending.

“Can we automate this?” is one of the most common questions I get.

Sure, we can. But that’s not the first thing you should ask.

Before you jump into workflow scripts or n8n hacks, zoom out:

- Does this process actually need to exist?
- How much time or cost will automation really save?
- Do you have clarity on the pain points you’re trying to remove?

Most teams skip that step. That’s how you end up automating messy processes, which only automate more mess.

AI Flow #4 is up. This week: how fast can you actually see ROI from AI?

I’m sharing one case we had with AI Flow: We helped a construction company cut proposal time from 4 hours to 20 minutes; they felt the ROI in week one.

But that's time savings. Revenue increase? Cost reduction? Each has a different timeline.

The problem is that most companies expect all three immediately, and that's why projects stall before showing value.

"We need AI to analyze our customer feedback."

This request came from a head of product at a 200-person company.

What they really had:

- Feedback in 7 different places
- Support tickets in Zendesk
- Feature requests in Productboard
- Sales calls in Gong
- User interviews in Dovetail
- Social mentions everywhere
- NPS surveys in their own tool
- Churn interviews in random Google Docs

Now, they wanted AI to magically connect all of it.

And here's what we told them: AI can't fix a structural problem.

First step: pick one source, the one with the highest signal (for them, it was support tickets because every issue was already tagged and categorized).

We built an AI analysis for that one source, and it worked. From that, they got insights they could act on.

Now they're adding the others. One at a time.

If you have data everywhere, don't start by connecting everything. Start by getting value from one source first.

Client last month: "We just need you to make our chatbot smarter."

Turns out their chatbot was giving wrong answers 40% of the time.

We dug deeper, and we found out that the problem wasn't the AI, but their knowledge base:

- Outdated pricing in 12 different PDFs
- Product specs scattered across Slack threads
- Support answers that contradicted each other

No model can fix messy data. It just makes confident mistakes faster.

What we did next was to spend a couple of weeks organizing their data before touching the AI. Now the chatbot works.

If your data lives in 10 different places with no single source of truth, start there.

A founder told me they spent 6 months building the perfect AI architecture, while never talking to a single user.

They launched last month, and it turns out users needed something quite different. 

Perfect systems built for the wrong problem are just expensive mistakes. I recommend prioritizing talking to users as much as possible to ensure you build what they really need.

A16z recently led a $30M round into Lio, a startup automating enterprise procurement with AI agents.

Sourcing, contract negotiation, spend management. All of it.

I'm not surprised by the funding. Procurement is one of the most automation-ready functions in a mid-size company, and one of the most overlooked.

When I talk to operations leaders about where AI could save them time, they usually mention customer-facing workflows first. Support, sales, marketing. Procurement comes up later, almost as an afterthought.

But when we actually map where decisions are slow and errors are expensive, procurement keeps appearing: requests sitting in inboxes waiting for approval, vendor comparisons done manually in spreadsheets, contracts reviewed line by line every single time.

The pattern underneath all of it is the same: high volume, repetitive structure, clear rules for what good looks like. That's exactly where automation compounds.

The question worth asking isn't whether procurement can be automated. It's which part of your procurement workflow is costing you the most time right now, and whether the process is clean enough to hand to an agent.

If the answer to the second part is no, that's where to start.

AI Flow #5 is up.

This week, I'm exploring the gap between demo and production, using a client situation as an example.

They had a recommendation engine already working in production. Users were getting suggestions, and the feature was live. But it performed at maybe 70% of where it could be.

After digging into the case, I noticed some problems in how it was built. It was rigid, with hard-coded paths, no observability, and every new requirement broke previous flows.

"We built a demo that works great. Now what?"

Now you find what breaks in production. 
What you should prioritize:

- Add monitoring
- Add fallback logic
- Test with real users, not perfect test cases.

Keep in mind that the gap between demo and production is where most projects fail.

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