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I built a portal to manage lectures and applications for teaching sociology students at my university. It displays educational content, monitors attendance, supports pedagogical evaluation, and provides training in methodological skills. Step-by-step: 1. I built a portal for managing lectures and applications for sociology students at my university. 2. I added educational content for students to access. 3. I included attendance monitoring. 4. I added tools for pedagogical evaluation. 5. I incorporated training in methodological skills.

I built an Opportunity Intelligence app with Claude Code for our sales team. Team members select the state and industry they want to target, and the app pulls live signals, such as whether a company is hiring for a specific role or opening a new facility, among others. The app then produces a top 10 list for further research, including a company overview, how our products work within that organization, battlecards, a custom LinkedIn introduction message, and a custom email message. From there, the team can research specific people at each organization. The app produces a top 10 list of targets with their LinkedIn profiles, phone numbers, and email addresses. Step-by-step: 1. I built an Opportunity Intelligence app with Claude Code for our sales team. 2. Team members select the state and industry they want to target. 3. The app pulls live signals, including whether companies are hiring for specific roles or opening new facilities. 4. The app generates a top 10 list of companies to research, with a company overview, how our products work within each organization, battlecards, a custom LinkedIn introduction message, and a custom email message. 5. Team members use that information to research specific people at each organization. 6. The app generates a top 10 list of targets with their LinkedIn profiles, phone numbers, and email addresses.

I built an AI workflow to automate guest-posting outreach. I use Claude to analyze target website requirements, draft personalized pitch emails for editors, and optimize my responses so I can pursue high-quality backlinks more efficiently. Step-by-step: 1. I provide Claude with the requirements for each target website. 2. I use Claude to analyze those requirements and identify what to address in the outreach. 3. I ask Claude to draft a personalized pitch email for the editor. 4. I use Claude to optimize my responses during the outreach process.

I turned an open-source XML lookup fix into a debugging walkthrough that other developers can run. I used Codex, Python, and GitHub to document a bug where a document style containing quotation marks could be saved but then fail during retrieval because its name was interpolated into an XPath expression. Step-by-step: 1. I gave Codex the original patch and inspected the affected code, tracing the stored value through the public API, the wrapper, and the XML library. 2. I extracted the smallest standalone reproduction with `lxml`. It included a name containing both single and double quotes and kept the failing lookup visible. 3. I replaced interpolation with a bound variable: `styles.xpath("style[@name=$name]", name=name)`. I checked that the wrapper forwarded variables while preserving its namespace mapping. 4. I ran both versions. The interpolated version raised `XPathEvalError` in my reproduction. Binding the value matched five exact names, while an absent name returned no match. 5. I published the explanation, executable example, and upstream patch together. I inspected the rendered article and links and disclosed AI assistance. A prompt to reuse: "Reduce this lookup failure to a runnable example. Keep the failing case, show the fix, and check ordinary text, both quote types, custom namespaces and a missing value. Report which checks actually ran." The original fix had already been merged, so this workflow documents it. Broader service behavior needs separate tests. I used my existing Codex and local Python setup and did not measure time savings. I am Hồ Khắc Huy, a freelance software engineer. This is my independent open-source work. The linked article includes the runnable example, upstream contribution, and my contact details: https://github.com/builtbyhuy/builtbyhuy/blob/main/notes/2026-09-06-xpath-variables.md

I built AIEnhancer to solve a problem I often encountered when working with low-quality images. Many images contain useful content but are too small, blurry, or lacking in detail to reuse effectively. The project uses AI-based image processing to improve image resolution and recover visual details. I wanted to make the workflow simple: upload an image, process it, and receive an enhanced version without needing professional image-editing software. One challenge was finding the right balance between sharpening details and avoiding artificial-looking results. During development, I experimented with different enhancement approaches and focused on keeping the output natural. I'm still interested in improving enhancement quality for different types of images. I'd like to hear how other developers handle image restoration and super-resolution, especially for difficult or heavily compressed images. Step-by-step: 1. I identified the problem of reusing images that were too small, blurry, or lacking in detail. 2. I built AIEnhancer to process low-quality images with AI-based image enhancement. 3. I designed the workflow around uploading an image, processing it, and receiving an enhanced version. 4. I experimented with different enhancement approaches to improve resolution and recover visual details. 5. I evaluated the results for a balance between sharper details and a natural appearance. 6. I continued exploring ways to improve enhancement quality for different image types, including difficult or heavily compressed images.

Most days, I want one capable general AI agent. The failure mode appears with higher-stakes questions in security, finance, law, or medicine: my main chat is already full of project context, the agent may answer confidently outside its depth, and a “specialist” prompt in the same thread still inherits the assumptions and tools that session has already loaded. I set up Hermes Agent with a consultation workflow that cold-starts an isolated specialist profile for a one-shot answer. I keep four advisors ready: security, financial, legal, and medical. Each call starts a separate process with its own model, tools, and skills. The specialist has no access to my parent conversation, so I have to write a self-contained prompt. It takes anywhere from a few seconds to a couple of minutes—not instantly—which is fine because I only use it when the stakes justify the wait. The gate is the part I care about most. Nothing runs until I explicitly name the profile, approve the exact prompt, and confirm. There is no silent auto-routing. When I want a second opinion, I draft my own take first, send the same question to the specialist, and compare where we agree and differ. This keeps the general agent honest and prevents specialty advice from becoming the default for routine work. I also keep the scope tight. I limit the medical profile to personal or educational questions, or fully de-identified material—not identifiable patient care. Routine work that my main agent already handles well never goes through consultation. I reserve the specialist profiles for deliberate second opinions or questions I do not want the default session answering alone. Step-by-step: 1. I work through the problem in my main Hermes session and write my own answer first when I want a second opinion. 2. If the question is high-stakes in security, finance, law, or medicine—or I simply want a deliberate second look—I choose the specialist profile to use. 3. I write a self-contained prompt with every fact and constraint the specialist needs because it cannot see the parent chat. 4. I name the profile, approve the prompt, and confirm before anything runs. 5. I let Hermes cold-start that profile as a separate process and return a one-shot answer. 6. I compare my draft with the specialist’s response: where we agree, where we differ, and what still needs a human professional. 7. I skip consultation for routine work and keep medical use limited to personal, educational, or de-identified material.

I built an AI meeting-intelligence workflow that turns my meetings, emails, chats, and handwritten notes into one living action tracker. Every action stays linked to its original source, and the workflow automatically generates a prep sheet for each meeting. Two problems drove me to build it. First, provenance: action lists are often disconnected from their sources. A line such as “follow up with vendor on pricing” is much less useful when I can’t open the Teams message or email it came from to review the details or reply. The context disappears as soon as the action is recorded. Second, I take most of my notes by hand because I think better on paper. Those notes used to be a dead end: they never entered any system, so the follow-ups disappeared. The workflow runs on a morning schedule or on demand before a meeting. I use Claude in Cowork with MCP connectors for email, chat, and calendar. It pulls recent messages and upcoming meetings while capturing the deep link or ID for each source item, so every action can be traced back to the exact email or Teams thread. For handwritten notes, I photograph the pages into a OneDrive folder and tag them. The workflow uses OCR and vision to extract the text and action items. Quick shorthand tags—`action`, `prep`, and `done`—on messages and photos tell it what to track or close. A Python layer then deduplicates the actions, assigns each one a stable ID, applies the tags, and updates the master tracker while preserving the source links. The result is one self-maintaining action list. I can click any item to return to its original artifact to reference the details or reply, and my paper notes finally flow into the same system. To recreate it, connect an AI assistant to your mail, chat, and calendar through MCP and store each source link with its action. Add a OneDrive photo drop with OCR for handwritten notes, use a simple tag vocabulary, and separate the responsibilities: let the AI handle capture and OCR, while plain code handles deduplication, IDs, and status updates. Step-by-step: 1. I scheduled the workflow to run each morning and added an option to run it on demand before a meeting. 2. I connected Claude in Cowork to my email, chat, and calendar through MCP so it could gather recent messages and upcoming meetings. 3. I captured the deep link or ID for every source item, allowing each action to trace back to the exact email or Teams thread. 4. I photographed handwritten notes into a tagged OneDrive folder and used OCR and vision to extract their text and action items. 5. I used the shorthand tags `action`, `prep`, and `done` on messages and photos to indicate what to track or close. 6. I used a Python layer to deduplicate actions, assign stable IDs, apply tags, and update the master tracker without removing source links. 7. I used the resulting tracker to return to the original artifact for each action and to bring paper-note follow-ups into the same system.

Je veux créer N’ose Digital IA, un SaaS international dédié à l’automatisation et conçu de A à Z comme un véritable business SaaS, avec l’IA au cœur de la plateforme. Le projet comprend un dashboard IA de service client ainsi qu’un agent vocal qui répond aux clients et s’appelle « Client ». Step-by-step: 1. Créer le SaaS N’ose Digital IA. 2. Optimiser la plateforme autour de l’automatisation. 3. Concevoir un dashboard IA dédié au service client. 4. Intégrer un agent vocal appelé « Client » pour répondre aux clients. 5. Développer la plateforme comme un business SaaS international, avec l’IA au cœur du projet.

I’ve been building AIEraser, a browser-based tool for removing unwanted elements from photos. One challenge has been making reconstructed areas blend naturally with nearby textures, especially when removing larger objects from detailed backgrounds. Early versions often left blurred patches, so I focused on improving contextual reconstruction while preserving the image’s original dimensions and sharpness. I also learned that selection flexibility matters. Some users prefer brushing over irregular objects, while others find box or automatic selection faster, so I added all three approaches. I’d appreciate feedback from anyone who has worked on image inpainting or object-removal tools. What types of images or backgrounds usually expose the biggest weaknesses in these models? I’m particularly interested in difficult test cases and suggestions for evaluating output quality. Step-by-step: 1. I built AIEraser as a browser-based tool for removing unwanted elements from photos. 2. I tested object removal on detailed backgrounds and identified blurred patches as a weakness, especially when removing larger objects. 3. I focused on improving contextual reconstruction while preserving the original image dimensions and sharpness. 4. I added brushing, box selection, and automatic selection to support different user preferences and object shapes. 5. I’m seeking difficult image and background test cases, along with suggestions for evaluating the quality of the output.

I’m building GradePath because schoolwork gets scattered quickly. A lecture, my notes, and the files for a class can all end up in different places. Then, when it’s time to study, I have to dig around to find what I need. The idea is simple: record a class, keep the recording and course materials together, and return to them later. If I ask Tutor about something, it should show me the relevant part of my notes, files, or class recording. If it can’t find an answer, it should say so. I also want students to be able to share useful study sheets with people who are actually in their class. I’m not trying to turn school into another content feed. I want to make it easier to keep up with a class and find the right material when I need it. Step-by-step: 1. I record a class. 2. I keep the recording, notes, and course materials together in GradePath. 3. I return to those materials when I need to study. 4. I ask Tutor about a topic and have it show the relevant part of my notes, files, or class recording. 5. If Tutor can’t find supporting material, it says so instead of presenting an unsupported answer. 6. I share useful study sheets with students who are in the same class.