Open BlixnexUsable artifacts
Turn broad objectives into working pages, product flows, demos, and deployed proof.
hermes session
Proof-of-work follow-up
for the Clarity AI conversation
with Jason Deegan
I build AI-assisted systems that combine LLMs, APIs, automation, and thoughtful product design to solve real business problems. My focus is reliable, polished, understandable software — not just generating code faster.
why_this_role
Why this role fits
"Applied AI is where I naturally spend my time: useful products, customer workflows, quality, and practical implementation."
Business problem first
I start with the bottleneck: what is slow, unclear, repetitive, or expensive for the people doing the work?
Builder's feedback loop
Build the workflow, verify the output, learn from usage, and improve the product.
Quality as proof
Working software matters, but so do polish, clarity, reliability, screenshots, tests, and review notes.
conversation_takeaways
Conversation Takeaways
"One thing I appreciated from today's conversation was seeing how Clarity AI approaches enterprise AI differently."
Practical AI harness
Rather than selling isolated AI features, Clarity gives organizations a way to build AI solutions inside their own environment.
Control and flexibility
The platform respects infrastructure ownership, data boundaries, and model choice — the details that matter in enterprise work.
Real feedback loop
Engineers solve customer problems while continuously improving the platform through real implementation.
opportunity_fit
Why This Opportunity Fits Me
I enjoy understanding how businesses operate, identifying bottlenecks, and building practical systems that improve the way people work.
What stood out during today's conversation was how closely that matches the way I already approach product development: work directly with real problems, improve the product through use, and take ownership of outcomes.
builder_philosophy
Builder Philosophy
I enjoy building products that people actually use. Speed matters. Quality matters. Clear communication matters. Good software is more than working code — it should solve real problems while feeling polished and trustworthy.
projects
Applied AI product environments
"Jason asked to see the other products. These are the clearest current examples of how I think through applied AI, workflow design, and product quality."
Blixnex
AI platform work for local service businesses: customer communication, workflow automation, demo rendering, and operational efficiency.
BlixFlex
AI-assisted trading workflow platform focused on structured execution, dashboards, analytics, and journaling concepts.
GitHub
Supporting artifact for project structure, technical work, and the engineering process behind the products.
blixnex_product
Blixnex: AI product work for local service businesses
"Blixnex is my main applied AI product environment for local service businesses: customer communication, workflow automation, operational efficiency, and polished software that feels trustworthy to the people using it."
Hover to explore. Open the full Blixnex site below.
Customer communication
Chat, SMS, and receptionist concepts designed around real service-business response gaps.
Operational workflow
Booking, follow-up, lead handling, and automation patterns that reduce manual work.
Quality bar
I care as much about product quality, clarity, and polish as I do technical implementation.
blixflex_product
BlixFlex: AI-assisted trading workflow platform
"BlixFlex explores AI-assisted trading workflows and financial decision support through structured execution, journaling concepts, dashboards, analytics, and workflow automation."
Captured screenshot. Open the full BlixFlex site below.
Structured execution
Explores workflows that help traders plan and review decisions more deliberately.
Dashboards + analytics
Another product surface for combining data, interface design, and automation into useful workflow software.
Honest status
A separate product experiment beyond Blixnex, not financial advice and not a claim of trading performance.
hermes_orchestration
Hermes orchestration workflow
"The hero shows the live terminal feel. This map shows the system: objective → orchestration → execution → verification → review."
Bill / voice promptSets objective, constraints, approval bar
capture + route
TelegramFast command surface
TypelessVoice capture when useful
Hermes Agent on VPSContext, memory, skills, tools
delegate + inspect
Claude Code / CodexImplementation agents
Repo + toolsFiles, CLI, browser, deployment
Human checkpointsBill approves important calls
verify + report
TestsRun checks before claiming done
Screenshots / R2 artifactsVisual evidence and handoff assets
Linear updatesProject tracking and next steps
Simple English reportWhat changed, passed, remains
// how_i_use_it
Hermes is my Telegram-connected orchestration agent running on my VPS. It coordinates Claude Code and Codex, checks outputs, verifies tests and screenshots, updates project tracking, and reports back in simple English.
// supporting_infrastructure
Hardened VPS environment
VS Code SSH workflow
Cloudflare Tunnel access
Agent orchestration
Restricted permissions
Human-in-the-loop review
// Framing note: this is my personal applied AI development workflow, not a claim of enterprise production infrastructure.
demo_render
Blixnex demo render: premium HVAC concept
"I built a system that generates high-fidelity demos for Blixnex. This render is a feature, not a separate product."
Hover to explore. Open the full render below.
What it is
A generated website preview for an HVAC concept, built as a Blixnex feature.
What it proves
Business direction to polished visual demo with screenshots and review notes.
Honest status
Demo artifact, not a live customer site. Some imagery is placeholder.
proof_of_shipping
Proof of Shipping
"I don't measure progress by lines of code. I measure it by working software, verification, review artifacts, screenshots, documented improvements, and reliable iteration."
Check before claiming
Validate links, layouts, screenshots, responsiveness, and behavior before calling work ready.
Improve through review
Use feedback, visual QA, and documented gaps to make the next version better.
Clear reporting
Explain what changed, what passed, what remains uncertain, and what should happen next.
github_artifact
GitHub: supporting engineering artifact
GitHub is one supporting artifact. The stronger signal is the engineering workflow behind the work: planning, implementation, verification, iteration, documentation, and continuous improvement.
clarity_fit
What I bring to Clarity AI
AI-native builder
Comfortable in fast-moving environments where AI is part of the engineering process, not a shortcut around judgment.
Verification mindset
Move quickly while validating outputs, testing assumptions, and documenting changes.
Product thinking
Connect technical implementation to business outcomes, user experience, and operational reality.
Clear communication
Explain technical work in practical language that customers and teammates can understand.
continuing_to_improve
Where I want to grow
Enterprise AI, customer-owned infrastructure, model flexibility, secure deployment patterns, and the engineering judgment needed to make practical AI reliable and useful.
"Practical AI, customer problems, and products that improve through use."
Today's conversation reinforced why Clarity AI is exciting to me: the work sits at the intersection of enterprise needs, hands-on engineering, customer bottlenecks, and platform improvement.
Email: billkadurujbs@gmail.com