Everest is the agentic AI platform for life science services—turn expertise into compliant workflows you can deploy internally or white-label into new
Users generally appreciate Log10 for its accuracy in parameter estimation, suggesting that it effectively meets their analytical needs. However, the lack of detailed user reviews and consistent feedback signifies that there might be limited visibility or engagement with broader aspects of the software, which could indicate room for improvement or greater market presence. Pricing sentiment is not directly addressed in the available mentions. Overall, Log10 seems to maintain a modest but positive reputation, particularly for its accuracy, without significant negative feedback.
Mentions (30d)
1
Reviews
0
Platforms
2
GitHub Stars
96
13 forks
Users generally appreciate Log10 for its accuracy in parameter estimation, suggesting that it effectively meets their analytical needs. However, the lack of detailed user reviews and consistent feedback signifies that there might be limited visibility or engagement with broader aspects of the software, which could indicate room for improvement or greater market presence. Pricing sentiment is not directly addressed in the available mentions. Overall, Log10 seems to maintain a modest but positive reputation, particularly for its accuracy, without significant negative feedback.
Features
Use Cases
Industry
information technology & services
Employees
11
Funding Stage
Seed
Total Funding
$7.2M
16
GitHub followers
14
GitHub repos
96
GitHub stars
20
npm packages
Parameter Estimate
The estimate seems quite accurate. Many people have noticed a drop in quality with GPT-5.1, GPT-5.2, GPT-5.3, and Opus 4.7. I think Gemini 2.5 Pro is a ~500B parameters. Its strong performance may come from its ability to search.
View originalI built an opensource Claude Code skill for Fiverr optimization since there wasn't any
Most "AI Fiverr tools" quietly tell the model to mentally estimate how many competitors a keyword has. That number is hallucinated — it changes every run. I wanted the opposite, so I built fiverr-gig-optimizer as a proper Claude Code skill (SKILL.md format, installable as a plugin). The core design rule: every market number — competition counts, demand signal, competitor pricing — comes from a deterministic Python script working on real data. The LLM layer never produces one. If the data isn't there, the skill asks you or says it doesn't have it. The model only writes copy (titles, descriptions) and your own offer choices (delivery times, package contents). How it works technically: query_dataset.py — keyword lookup against the bundled sample dataset, returns a gig_count + match_confidence (HIGH/MEDIUM/LOW). Low confidence → the skill asks you to paste the Fiverr count instead of guessing. score_keyword.py — piecewise-linear competition score over log10(gig_count), anchored so tier labels map intuitively. All constants in a tunable JSON config. analyze_pricing.py — per-tier percentiles (p25/median/p75) of real competitor prices. Too few samples → flagged low-confidence, not fabricated. A vendored Perseus/__NEXT_DATA__ reader for live scraping that uniquely recovers the real "X services available" search total — something no off-the-shelf Apify actor returns. There's also an opt-in community dataset on Hugging Face. Scrape your niche, strip PII, contribute back — the free bundled data gets better over time. Install: /plugin marketplace add Ahad690/fiverr-gig-optimizer /plugin install fiverr-gig-optimizer@fiverr-tools GitHub: https://github.com/Ahad690/fiverr-gig-optimizer Dataset: https://huggingface.co/datasets/Ahad690/fiverr-gigs Would love feedback on the SKILL.md structure and the scoring formula — both are visible and tunable. Happy to answer questions about the Perseus reader approach for gig_count_in_search if anyone's interested. submitted by /u/Nocare420 [link] [comments]
View originalParameter Estimate
The estimate seems quite accurate. Many people have noticed a drop in quality with GPT-5.1, GPT-5.2, GPT-5.3, and Opus 4.7. I think Gemini 2.5 Pro is a ~500B parameters. Its strong performance may come from its ability to search.
View originalRepository Audit Available
Deep analysis of log10-io/log10 — architecture, costs, security, dependencies & more
Log10 uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Schedule a demo.
Log10 is commonly used for: Real-time monitoring of application performance, Automated incident response and resolution, Data visualization for enhanced decision-making, Compliance tracking for regulatory requirements, Predictive analytics for resource allocation, Collaboration tools for cross-functional teams.
Log10 integrates with: Slack, Jira, GitHub, AWS CloudWatch, Google Cloud Monitoring, Azure Monitor, Datadog, Prometheus, Grafana, PagerDuty.
Log10 has a public GitHub repository with 96 stars.
