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Advanced the automation strategy and framework for a fintech trading platform: built scalable Selenium + Java test suites integrated with CI/CD and set up management dashboards for defects, automation backlog and results. Result: test coverage increased by 30%.
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AetherOS is a web desktop for AI agents. The entire project sits inside a single HTML file. It starts with a boot screen. Then it opens into a dark workspace. The background has black animated particles. Everything runs on plain CSS and vanilla JavaScript. The site loads zero external dependencies and zero sound files.
The bottom dock holds seven apps. These tools manage four AI agents: Atlas, Sage, Bolt, and Vega. The Agents app shows them chatting and passing tasks. Files lets you browse a drive. Memory shows a live graph of what the system knows. Users can also modify a history timeline. Studio lets you test prompts. Flows builds node pipelines, and Cost tracks token usage.
Every window can be moved, resized, minimized, or maximized. The top menu bar has a model switcher. It routes tasks to Claude Opus 4.7, GPT-5.2, Gemini 3 Pro, DeepSeek V4, or Llama 4. The OS also has Spotlight search, sound toggles, and local API key inputs. The interface uses dark glass panels with blue highlights.
Building the node graphs in one single HTML5 file was tough. At first, the physics nodes pushed apart too hard. They floated right off the screen. Dragging extra lines while getting zoomed in also threw off the precise positions. I fixed the center gravity math. Then I divided mouse positions by the zoom factor. That stopped the unnecessary movement and kept the OS stable.😊
𝐉𝐨𝐛 𝐡𝐮𝐧𝐭𝐢𝐧𝐠 𝐨𝐧 𝐡𝐚𝐫𝐝 𝐦𝐨𝐝𝐞
B2 English, based in Argentina. Payment preferably in USDT. Remote only. Quite a combination, right?
I built an AI-assisted job-search system around Freehire and Hirify. Both platforms offer official API/CLI access for automation, which makes them much easier to connect to an agent workflow. Simple Python scripts collect job listings and run the first pass of filtering against strict rules.
Then agents double-check the requirements, match them against my experience, and tailor my CV to each role. They highlight relevant projects and technologies I've actually worked with. Final review and approval stay with me.
𝐀𝐬 𝐨𝐟 𝐒𝐞𝐩𝐭𝐞𝐦𝐛𝐞𝐫 𝟐𝟔:
✅ 20 applications sent;
✅ 8 HR screenings passed;
✅ 1 video screening completed and submitted.
🚀 Technical interviews are still ahead.
I'm not ready to write off the market. Even with my constraints, things are moving.
A job search deserves 𝐚𝐧 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨𝐨: clear rules, automation, and checks on the results.
$238K in revenue in month one, and costs down 47%.
That's what happened when a US life insurance agency moved from a stack of separate tools onto one AI sales floor I built.
- An AI voice agent screens every caller and fills the CRM live
- Supervisors can barge in, whisper, transfer or conference on any live call
- Every application tracked from submitted to paid
- Commissions calculate themselves on approval
- Publishers get their own portal and automatic end-of-day reports
Full case study on my profile.