{
  "publisher": "MEKreflect",
  "author": "Haris Mekic",
  "published": "2026-10-16",
  "repository": "https://github.com/Haris88m/mekreflect-labs",
  "scope": "Source-derived research packages and synthetic tests. Historical hosted observations are separate from offline verification.",
  "studies": [
    {
      "id": "observer8-context-handoffs",
      "title": "When context moves between AI workers",
      "label": "OBSERVER8",
      "question": "Can a workflow distinguish a missing source, conflicting instructions and a genuinely unfinished submission?",
      "origin": "I developed bidding workflows around the decisions a bid manager has to make. An answer can sound careful while carrying an old deadline or budget into the next step. I wanted to inspect that gap, not just improve the wording of the answer.",
      "method": "I separated the source pack, the receiving context, the worker response and the checks applied afterwards. Fictional bidding cases make it possible to change an amendment, authority rule or evidence requirement without exposing a company’s tender material.",
      "workflow": [
        "Define the source pack and its versions",
        "Give each worker a role and receiving context",
        "Inspect the fields passed to the next role",
        "Recover missing context and reassess readiness"
      ],
      "implementation": "The public laboratory contains source-derived Python evaluators, fictional cases and offline checks. Its provenance records identify the original implementation and the changes made for publication.",
      "evidence": "The separately published historical readout summarizes 23 hosted AI tasks across three experiments. The offline test suite checks the evaluator and its fixtures; it does not repeat those hosted model calls or establish a general model ranking.",
      "limits": "Some readiness cases remain disputed. Source availability, current fields and reconciliation of finished documents are separate questions. A replay is not a new live-agent experiment.",
      "next": "Have independent reviewers define readiness, then compare context strategies on held-out cases, including cases that should pass without unnecessary blocking.",
      "code": "https://github.com/Haris88m/mekreflect-labs/tree/main/observer8",
      "related": "/evaluations/"
    },
    {
      "id": "operational-completion-controls",
      "title": "An attempted action is not a completed result",
      "label": "Career operations research",
      "question": "What should a digital worker have to show before work can be marked complete?",
      "origin": "I built a career operations system because finding work itself involves research, source evidence, preparation, approvals and follow-through. Using it made the difference between a submitted form and a confirmed result very concrete.",
      "method": "I treated claims, duplicate checks and completion receipts as operational records. The public example uses synthetic cases so the state transitions can be tested without publishing anyone’s CV, correspondence or application history.",
      "workflow": [
        "Connect a claim to its evidence",
        "Check whether the case already exists",
        "Keep approval separate from execution",
        "Verify the receipt before recording completion"
      ],
      "implementation": "Claim-wording and receipt checks are adapted from the existing Python implementation. A newly written in-memory SQLite harness makes the duplicate and state-transition gates inspectable. It is not the original database or complete submission engine.",
      "evidence": "Offline regression tests exercise synthetic records. They demonstrate the published control logic, not that an external application was submitted or accepted.",
      "limits": "Receipt text matching is a heuristic. A stored digest identifies the exact text but does not authenticate its sender or establish its truth. Separate provenance and outcome confirmations remain mandatory.",
      "next": "Connect these checks to independently verifiable external receipts and evaluate false rejections as carefully as false completion.",
      "code": "https://github.com/Haris88m/mekreflect-labs/tree/main/operations",
      "related": "/research/evidence-decisions/"
    },
    {
      "id": "paid-ai-admission-control",
      "title": "Do not spend when the permission check fails",
      "label": "Operational reliability",
      "question": "Should a paid model request proceed when its usage-limit check is unavailable?",
      "origin": "My research includes the ordinary controls that keep an AI workflow usable. A helpful draft is not a good result if the system ignores the rule that was meant to control its cost.",
      "method": "I examined the draft-generation boundary and tested the actual failure paths. The public example isolates the admission decision from the provider and database, using controlled responses instead of live credentials.",
      "workflow": [
        "The original service verifies identity before admission",
        "Ask the usage limiter for a decision",
        "Require an explicit valid allow",
        "Call the model only after admission"
      ],
      "implementation": "A JavaScript admission function extracted from the TypeScript handler uses injected callbacks to distinguish an explicit denial from a failed or malformed limiter response. The local example omits production authentication and networking. Its offline tests include malformed adapters, retry bounds and checks that generation is not called after rejection.",
      "evidence": "The original focused handler regression produced eight failures and two passes. The repaired private handler passed all ten cases. The public adaptation has its own reproducible tests and provenance rather than borrowing a whole-application security claim.",
      "limits": "This is one spending-control boundary. It does not establish production authentication, tenant isolation, provider uptime or a complete application security audit.",
      "next": "Measure rejected requests, recovery behaviour and spend within a properly authenticated integration test environment.",
      "code": "https://github.com/Haris88m/mekreflect-labs/tree/main/operations",
      "related": "/research/operational-integrity/"
    },
    {
      "id": "reproducible-visual-production",
      "title": "Turn an idea into a repeatable visual output",
      "label": "Creative tooling research",
      "question": "How can a small visual-production pipeline produce an inspectable result without depending on a live model service?",
      "origin": "I explore ideas by making something I can see and test. One part of that work was a Python rendering tool that turns prepared images into a short moving sequence.",
      "method": "I separated the rendering mechanism from the private creative material. The public demonstration uses generated geometric images, so another person can reproduce the workflow without needing the original artwork or a family story.",
      "workflow": [
        "Prepare synthetic image inputs",
        "Configure the sequence and movement",
        "Render through the local media pipeline",
        "Inspect the output and failure cases"
      ],
      "implementation": "The published Python renderer is derived from the existing Pillow and FFmpeg-based tool. Publication work adds explicit input validation, safer temporary-file handling and tests.",
      "evidence": "The repository documents the commands and local render checks. Rendering is a media-processing operation, not image-model training or evidence of audience impact.",
      "limits": "Original artwork, personal stories and binary releases are not included. Users must have rights to their own inputs and review the third-party dependency licenses.",
      "next": "Evaluate repeatability, rendering time and accessibility of visual explanations across different input formats.",
      "code": "https://github.com/Haris88m/mekreflect-labs/tree/main/creative-tools",
      "related": "/method/"
    },
    {
      "id": "telemetry-context-boundaries",
      "title": "When a missing byte becomes a believable answer",
      "label": "Telemetry research",
      "question": "Can a parser turn an incomplete response into a plausible measurement by joining unrelated context?",
      "origin": "I explored diagnostic software to understand when a response contains usable information. The important failure was a short frame borrowing bytes from the next response and producing a believable RPM value.",
      "method": "I extracted the existing Java conversion module and gave it synthetic responses. I checked each response boundary before converting its bytes, then tested whether a complete response could still be read after an incomplete one.",
      "workflow": [
        "Receive an individual synthetic response",
        "Preserve its frame boundary and validate every byte",
        "Convert supported fields or return unresolved",
        "Read a complete response and inspect recovery"
      ],
      "implementation": "The public Java module retains seven existing conversion methods. The repair isolates response lines, validates exact hexadecimal byte tokens, checks diagnostic-code payload lengths and bounds response size. The demonstration and assertion harness are new.",
      "evidence": "Seventeen Java assertions pass. Before the correction, a truncated frame followed by a different response produced 1680.25 RPM. The corrected parser returns unresolved for that input and reads the next complete test frame as 2048 RPM.",
      "limits": "All input is synthetic and local. CAN framing, multiple ECU arbitration, transport and physical diagnosis are outside this parser. The result demonstrates a parsing boundary rather than a tested vehicle application.",
      "next": "Expand the supported protocol only against documented frame contracts and independent reference cases, keeping transport, decoding and interpretation separately testable.",
      "code": "https://github.com/Haris88m/mekreflect-labs/tree/main/telemetry-parser",
      "related": "/research/context-handoffs/"
    },
    {
      "id": "current-evidence-gates",
      "title": "A reference is not a current observation",
      "label": "Evidence and expiry research",
      "question": "What must a source record contain before it can support a current operational decision?",
      "origin": "In Signal I separated discovering a reference from knowing whether a source can support a current decision. I made permission, freshness and quantity explicit in the software because each answers a different question.",
      "method": "I extracted the existing decision gate and tested invented records at expiry, with missing permission, malformed dates and quantities the runtime cannot represent exactly. The gate evaluates supplied evidence without refreshing or inventing it.",
      "workflow": [
        "Read the source label and explicit caller policy",
        "Validate the observation time and source-defined expiry",
        "Check quantity, price and exact permission flags",
        "Return eligibility with expired or unsupported records excluded"
      ],
      "implementation": "The JavaScript module keeps the original source-host policy, strict flags, fixture opt-in and 60-second clock-skew rule. Publication repairs require valid canonical UTC timestamps, reject source URLs containing credentials and require safe integer quantities.",
      "evidence": "Nine Node tests pass. Three tests reproduced failures in the original module before correction. The runnable demonstration distinguishes a current synthetic record, an expired record and a record with unknown quantity.",
      "limits": "A supplied authorization flag is an assertion, and a hostname match does not verify a commercial agreement or a live feed. This local module performs no network requests, reservations, payments or health decisions.",
      "next": "Connect server-owned source policies to independently verified observations and measure both incorrect acceptance and useful records that get rejected.",
      "code": "https://github.com/Haris88m/mekreflect-labs/tree/main/evidence-gates",
      "related": "/research/evidence-decisions/"
    }
  ]
}