My job board failed re-qualification. The system reported: "FAIL: only 19.3% still live after removing expired and stale postings (need >=40.0%)". This verdict, "REJECTED", was delivered by job-board-watch.log 32.5 hours ago. It meant a wired board, designed to surface relevant opportunities, was deemed non-viable because less than half of its listed jobs were genuinely active. The threshold for viability was 40.0%. We missed it by a critical margin.
The job-board-watch Process
The job-board-watch process is one of nine online processes supervised by PM2. It runs alongside critical components like cto-aipa (which has seen 223 restarts and is up 0 days) and algom-stream (with 55193 restarts over 66 days). While algom-stream's restarts are a known anomaly, job-board-watch's failure was a direct functional block. Its purpose is simple: audit the live status of job postings and ensure a minimum quality bar. If the Job Board Live Percentage Threshold isn't met, the board is flagged.
Defining "Live" Postings
For job-board-watch, a "live" posting means it's not expired and not stale. The system automatically removes these. The specific criteria for "expired" and "stale" are internal to the job-board-watch logic and are not detailed in the current logs. However, the outcome is clear: 19.3% of the postings met these criteria. This figure is a direct measurement, not an estimate. The system's expectation was 40.0%. This gap highlights a fundamental issue in either the sourcing, the expiration logic, or the refresh rate of the board itself.
Impact on Deal Flow and Applications
The failure to meet the 40.0% threshold has downstream effects. My deal-digest.log shows 28 live job deals. These are the opportunities that have progressed through various stages in HubSpot: 13 in "qualifiedtobuy", 6 in "decisionmakerboughtin", 7 in "contractsent", and 2 in "appointmentscheduled". While the deal-digest itself reports 28 live job deals, the job-board-watch failure indicates that the underlying pool of available jobs is not healthy enough to sustain consistent deal flow. The apply-kit-audit.log shows recent successful applications, for example, to "Implementation / Solutions Consultant @ Puente Talent Partne" and "Technical Account Manager @ Puente Talent Partners". These applications depend on a robust, live job board. A rejected board means fewer quality opportunities for the apply-queue to process, which recently sent 3 new and 4 new items to Telegram.
The Role of AI Agents and Data Freshness
My AI agents are constantly working to find and process jobs. The cto-aipa process, despite its 223 restarts, is actively being developed, with 12 commits in the last 48 hours. Recent commits include "WHITESPACE LatAm Spanish search + relevance live" and "WHITESPACE background-finish live". These indicate ongoing efforts to improve job sourcing and data processing. The serpapi-jobs process, which is online and has 6 restarts, is likely a key component in fetching job data. However, if the data sources themselves have a high rate of expired or stale postings, or if the job-board-watch's definition of "live" is too stringent for the available data, then this 19.3% figure will persist. The algom-poll process, online for 85 days with 0 restarts, suggests a stable polling mechanism, but stability doesn't guarantee data quality.
Next Steps: Addressing the Live Percentage
To address the low Job Board Live Percentage Threshold, I need to investigate several areas:
1. Source Quality: Are the job sources providing a high volume of already expired or soon-to-be-expired postings? This would require auditing the raw data from serpapi-jobs or other sources.
2. Expiration Logic: Is the internal logic for identifying "expired" and "stale" postings too aggressive? A review of the job-board-watch code is necessary.
3. Refresh Frequency: How often are postings being re-validated or removed? If the refresh cycle is too slow, many jobs might become stale before the system can update their status.
4. Agent Feedback Loop: Can the cto-aipa agent be trained to prioritize jobs with longer expected live durations or to flag sources known for high churn? The NOW.md file, which serves as working memory for my Cursor and Claude Code agents, indicates a need for better communication and shared context between these agents. This could extend to sharing insights on job board quality.
The job-board-watch failure is a clear signal that the underlying data pipeline for job postings needs attention. Achieving the 40.0% threshold is not just about a number; it's about ensuring the utility and reliability of the job board for its users and for my own automated application processes.
Frequently Asked Questions
Q: What caused the job-board-watch process to fail re-qualification?
A: The process failed because only 19.3% of the job postings were still live after removing expired and stale entries, falling short of the required 40.0% viability threshold. This was reported in job-board-watch.log 32.5 hours ago.
Q: How does the system define "live" postings?
A: A "live" posting is one that has not been identified as expired or stale by the job-board-watch process. The specific criteria for expiration and staleness are internal to the process's logic.
Q: What is the impact of this failure on job applications?
A: A rejected job board means a reduced pool of verified, active job opportunities. This directly affects the effectiveness of automated application processes and the quality of leads for "live job deals," which currently stand at 28.
Q: Are there any other processes indicating issues with data freshness or quality?
A: The algom-stream process has seen 55193 restarts over 66 days, which is a known anomaly. While not directly linked to the job board's live percentage, it suggests potential instability in data streaming components that could indirectly affect data freshness.
Q: What is the immediate next step to fix this?
A: The immediate next step is to investigate the job-board-watch's internal logic for defining "expired" and "stale" postings, as well as auditing the quality and freshness of the job data sources.