UNIFY SUITE: AI workforce on Vultr
UNIFY SUITE is an AI workforce for the five departments most companies run on: Sales, Finance, HR, Support and Procurement. Each gets its own agent. The Lead Qualifier rates leads and cites its evidence, the Invoice Processor reads supplier invoices, the Resume Screener quotes the resume for every skill it credits, the Ticket Investigator checks a customer's real orders, and the Vendor Comparator explains a ranking that code computes. Aura, a chat assistant, routes plain-English requests to the right agent and can only do what the signed-in user may do. The rule is simple: AI reads, code decides, humans approve. The model only extracts, classifies and drafts, and every reply must pass a schema. Matching, ranking, duplicate checks and permissions are plain, tested Python. Payments, refunds and purchase orders wait in an Approval Center for a manager. The main flow is supplier payment verification. A clerk uploads an invoice, the AI reads it, code matches it to the purchase order and blocks duplicates, and the run pauses for a finance manager. Approve, and a payment is created and verified. Reject, and verification proves no money moved. The Command Center logs every agent and tool call, and the audit log is append-only. Every AI result has a Why? panel that shows what the AI read and what code decided. Drafted emails pass a quality gate in code, so a reminder can never quote an amount that is not in the facts. Hidden instructions in an invoice or call transcript are flagged to the manager. Everything runs on Vultr. One Cloud Compute VM hosts the app (React, FastAPI and PostgreSQL in Docker), Coolify from the Vultr Marketplace deploys it from GitHub with HTTPS, and every agent uses glm-5.3-flash through Vultr Serverless Inference. On a fictional data set with an answer key, 57 of 57 invoices were handled correctly, and 406 automated tests cover the approve and reject paths.
Category tags:Ghostfox Zero — M3+M4 Retrieval Portfolio
Ghostfox Zero is a two-match retrieval portfolio built entirely on AMD ROCm — one resident Qwen3-4B-Instruct server in bf16 on the AMD GPU, one BM25-first evidence pipeline, two containerized task front ends. MATCH 4 (Intelligent Web Information Retrieval): the agent navigates a saved copy of a website like a browser — every HTML page parsed, all navigation links followed, full BM25 index built over cleaned text, zero downloads at evaluation time. Given a natural-language query it extracts the exact value requested (name, date, price, version, phone number) with the source page as citation. MATCH 3 (Retrieval-Augmented Generation): the same fail-cited pipeline extended to mixed-document corpora — PDF (pypdf), DOCX and XLSX (stdlib zip+xml), CSV, source code, logs and config files all indexed with the same BM25 core, returning the short answer plus the exact files it came from, and nothing when the corpus does not contain the answer. Both containers implement the full league contract: phase 1 indexes the corpus and starts the resident model server within the startup budget, phase 2 answers each query as a separate process within the time limit, writing output JSON with answer, citations and confidence. Fail-closed by design: exact values only, citations always, empty answer instead of a guess — evidence-first where naive LLM agents hallucinate.
autokeren-solo
WHYtHON
Maximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCode Maximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing
not sure
AetherTactics: Low-Compute Gemini Agent
AetherTactics is an autonomous strategy AI agent engineered for the AMD AI League Match 6 challenge: "Can Your AI Master the Unknown?". The agent navigates an unfamiliar, procedural fog-of-war grid, evades environmental hazards, recovers mission-critical alien relics, and safely reaches extraction points while aggressively minimizing compute overhead and token consumption. Powered by Google Gemini (supporting Gemini 2.5 Flash, 3.1 Flash-Lite, and custom endpoints), AetherTactics utilizes an ultra-compact radar serialization protocol. Instead of verbose context passing, it translates 2D exploration states into dense, minified matrices, constraining LLM reasoning to strict micro-JSON directives under 60 output tokens. To prevent agent freezes during live competition, the platform incorporates an automated watchdog failover pipeline. If a model encounters 503 high-demand or rate-limit spikes, the system automatically executes exponential backoff and fails over to stable alternatives without interrupting autonomous navigation. Built-in live telemetry monitors prompt tokens, generation tokens, step latency, and turn efficiency to deliver state-of-the-art decision-making on minimal inference budgets.
Solo Builder XP
Pinpoint: Verified Web Answer Agent on AMD ROCm
Most web agents summarize or hand back a list of links. Pinpoint returns the one value you asked for (a price, a date, a version, a phone number) together with the page and the exact quote it came from. If it cannot verify the value, it returns an empty answer instead of guessing. How it works: each question starts by writing a valid placeholder JSON, so a crash never breaks the output. The agent fetches the start page and up to 8 same-site links ranked by how well their link text matches the question. It switches to a headless Chromium browser only when a page renders with JavaScript. Pages are cleaned to text with table rows kept, and passages are ranked with BM25. A question classifier decides the expected answer type, and Qwen3-8B, served locally on an AMD Instinct GPU with ROCm, picks a candidate value from the top passages. The answer is accepted only if it appears verbatim in the cited passage and matches the expected type. Otherwise a rule-based extractor is tried, and then an empty answer. The model loads once at container start, and every question runs under a hard deadline. No external API keys are used: all inference runs on the AMD GPU. Results on our open 40-question benchmark across [X] real sites: [X]% exact match, 0 fabricated answers, 8/8 correct empty answers on unanswerable questions, median [X] s per question. The benchmark, eval script and Docker image are open source.
Pauprint
VeriFetch ROCm: Cite-or-Refuse Web Agent
VeriFetch ROCm v3.0 — Enterprise-Hardened 5-Match Agent Suite (Matches 2–6) on AMD Instinct MI300X (rocm/pytorch:rocm10.0). Built 100% locally on AMD Instinct MI300X (192 GB HBM3, bfloat16) with zero paid API keys, VeriFetch ROCm runs behind a warm Unix-socket daemon (/tmp/verifetch.sock, 0.42s latency) with continuous 1.25 GiB ROCm GPU VRAM residency. 1. MATCH 4 — INTELLIGENT WEB INFORMATION RETRIEVAL (ERIX-12/verifetch-rocm) - Full-DOM & Multi-Hop BFS Crawler (extractor.py): Indexes HTML <table> ("[Row | Col]: Val"), <dl>/<dt>/<dd>, JSON-LD (<script type="application/ld+json">), and <p>/<li> blocks, plus depth-1 same-domain crawling (up to 4 subpages) ranked by IDF-weighted BM25. - Enterprise Guardrails & Cryptographic Proof: Enforces SSRF private-IP blocking, a 2 MB response cap, NFC Unicode normalization, an Entity-Attachment Gate (preventing cross-row entity mismatch), and SHA-256 content proof hashes (content_sha256). - Cite-or-Refuse Substring Verifier: Verifies literal char-for-char DOM support or abstains with structured abstain_reason codes (10/10 offline & adversarial tests passing, 0.0% hallucination). 2. MATCH 3 — RETRIEVAL-AUGMENTED GENERATION (ERIX-12/rocm-corpus-rag) - Offline Mixed-Document RAG (rag_engine.py): Indexes .pdf, .docx, .xlsx, .csv, .txt/.log, .py, and .png/.jpg files while safely skipping chmod 000, encrypted, and WITHDRAWN files (200/200 on all 10 Match 3 sample questions). 3. MATCH 2 — OPTICAL CHARACTER RECOGNITION (ERIX-12/rocm-vision-ocr) - VLM Plate & Sign Reader (ocr_engine.py): Processes PNG, JPEG, and TIFF images, stripping US state banners while preserving Chinese province prefixes (200/200 on all 10 Match 2 sample images). 4. MATCH 5 & MATCH 6 (ERIX-12/rocm-repo-repair & ERIX-12/rocm-unknown-agent) - Match 5: Multi-agent AST fault localizer & pytest-verified diff synthesizer. - Match 6: Symbolic world-rule learner & A* frontier planner (92% token reduction).
VeriFetch ROCm
For Innovators & Creators
Ghostfox Zero — M3+M4 Retrieval Portfolio
Ghostfox Zero is a two-match retrieval portfolio built entirely on AMD ROCm — one resident Qwen3-4B-Instruct server in bf16 on the AMD GPU, one BM25-first evidence pipeline, two containerized task front ends. MATCH 4 (Intelligent Web Information Retrieval): the agent navigates a saved copy of a website like a browser — every HTML page parsed, all navigation links followed, full BM25 index built over cleaned text, zero downloads at evaluation time. Given a natural-language query it extracts the exact value requested (name, date, price, version, phone number) with the source page as citation. MATCH 3 (Retrieval-Augmented Generation): the same fail-cited pipeline extended to mixed-document corpora — PDF (pypdf), DOCX and XLSX (stdlib zip+xml), CSV, source code, logs and config files all indexed with the same BM25 core, returning the short answer plus the exact files it came from, and nothing when the corpus does not contain the answer. Both containers implement the full league contract: phase 1 indexes the corpus and starts the resident model server within the startup budget, phase 2 answers each query as a separate process within the time limit, writing output JSON with answer, citations and confidence. Fail-closed by design: exact values only, citations always, empty answer instead of a guess — evidence-first where naive LLM agents hallucinate.
autokeren-solo
WHYtHON
Maximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCode Maximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing credit only Only when I open OpenCodeMaximum power, size flexible Is the $50 a credit balance inside Render $50 existing
not sure
AetherTactics: Low-Compute Gemini Agent
AetherTactics is an autonomous strategy AI agent engineered for the AMD AI League Match 6 challenge: "Can Your AI Master the Unknown?". The agent navigates an unfamiliar, procedural fog-of-war grid, evades environmental hazards, recovers mission-critical alien relics, and safely reaches extraction points while aggressively minimizing compute overhead and token consumption. Powered by Google Gemini (supporting Gemini 2.5 Flash, 3.1 Flash-Lite, and custom endpoints), AetherTactics utilizes an ultra-compact radar serialization protocol. Instead of verbose context passing, it translates 2D exploration states into dense, minified matrices, constraining LLM reasoning to strict micro-JSON directives under 60 output tokens. To prevent agent freezes during live competition, the platform incorporates an automated watchdog failover pipeline. If a model encounters 503 high-demand or rate-limit spikes, the system automatically executes exponential backoff and fails over to stable alternatives without interrupting autonomous navigation. Built-in live telemetry monitors prompt tokens, generation tokens, step latency, and turn efficiency to deliver state-of-the-art decision-making on minimal inference budgets.
Solo Builder XP
Pinpoint: Verified Web Answer Agent on AMD ROCm
Most web agents summarize or hand back a list of links. Pinpoint returns the one value you asked for (a price, a date, a version, a phone number) together with the page and the exact quote it came from. If it cannot verify the value, it returns an empty answer instead of guessing. How it works: each question starts by writing a valid placeholder JSON, so a crash never breaks the output. The agent fetches the start page and up to 8 same-site links ranked by how well their link text matches the question. It switches to a headless Chromium browser only when a page renders with JavaScript. Pages are cleaned to text with table rows kept, and passages are ranked with BM25. A question classifier decides the expected answer type, and Qwen3-8B, served locally on an AMD Instinct GPU with ROCm, picks a candidate value from the top passages. The answer is accepted only if it appears verbatim in the cited passage and matches the expected type. Otherwise a rule-based extractor is tried, and then an empty answer. The model loads once at container start, and every question runs under a hard deadline. No external API keys are used: all inference runs on the AMD GPU. Results on our open 40-question benchmark across [X] real sites: [X]% exact match, 0 fabricated answers, 8/8 correct empty answers on unanswerable questions, median [X] s per question. The benchmark, eval script and Docker image are open source.
Pauprint
VeriFetch ROCm: Cite-or-Refuse Web Agent
VeriFetch ROCm v3.0 — Enterprise-Hardened 5-Match Agent Suite (Matches 2–6) on AMD Instinct MI300X (rocm/pytorch:rocm10.0). Built 100% locally on AMD Instinct MI300X (192 GB HBM3, bfloat16) with zero paid API keys, VeriFetch ROCm runs behind a warm Unix-socket daemon (/tmp/verifetch.sock, 0.42s latency) with continuous 1.25 GiB ROCm GPU VRAM residency. 1. MATCH 4 — INTELLIGENT WEB INFORMATION RETRIEVAL (ERIX-12/verifetch-rocm) - Full-DOM & Multi-Hop BFS Crawler (extractor.py): Indexes HTML <table> ("[Row | Col]: Val"), <dl>/<dt>/<dd>, JSON-LD (<script type="application/ld+json">), and <p>/<li> blocks, plus depth-1 same-domain crawling (up to 4 subpages) ranked by IDF-weighted BM25. - Enterprise Guardrails & Cryptographic Proof: Enforces SSRF private-IP blocking, a 2 MB response cap, NFC Unicode normalization, an Entity-Attachment Gate (preventing cross-row entity mismatch), and SHA-256 content proof hashes (content_sha256). - Cite-or-Refuse Substring Verifier: Verifies literal char-for-char DOM support or abstains with structured abstain_reason codes (10/10 offline & adversarial tests passing, 0.0% hallucination). 2. MATCH 3 — RETRIEVAL-AUGMENTED GENERATION (ERIX-12/rocm-corpus-rag) - Offline Mixed-Document RAG (rag_engine.py): Indexes .pdf, .docx, .xlsx, .csv, .txt/.log, .py, and .png/.jpg files while safely skipping chmod 000, encrypted, and WITHDRAWN files (200/200 on all 10 Match 3 sample questions). 3. MATCH 2 — OPTICAL CHARACTER RECOGNITION (ERIX-12/rocm-vision-ocr) - VLM Plate & Sign Reader (ocr_engine.py): Processes PNG, JPEG, and TIFF images, stripping US state banners while preserving Chinese province prefixes (200/200 on all 10 Match 2 sample images). 4. MATCH 5 & MATCH 6 (ERIX-12/rocm-repo-repair & ERIX-12/rocm-unknown-agent) - Match 5: Multi-agent AST fault localizer & pytest-verified diff synthesizer. - Match 6: Symbolic world-rule learner & A* frontier planner (92% token reduction).
VeriFetch ROCm