AI hype is loud, but real value comes from measurable outcomes, not buzzwords. A Dev's guide to spotting AI-washing lays out red flags that actually matter [1].
Red Flags to Watch For - They can't explain the 'how'—no specifics on methods like NLP models, forecasting, or data pipelines [1]. - They pitch features, not outcomes—no link to measurable gains like latency or error reduction [1]. - The 'magic black box' defense—proprietary data or training details dodge governance questions [1]. - The 'AI island' architecture—no clear integration with existing systems or workflows [1]. - No real-world proof—case studies lack relevance or measurable results [1].
Demand Transparency and Real Proof - Ask about data models, training approaches, and explainability; vendors should discuss them without surrendering IP [1]. - Demand detailed, relevant case studies with measurable results from a company similar in scale [1].
APIs vs Local and Quantization Realities - Avoid API sources that withhold quantization or degrade model quality; official routes tend to be more trustworthy [2]. - They’re talking quantization, with mentions of FP8 and variability; check metadata to see if changes impact quality [2]. - Be mindful of data-retention controls; some services offer toggles to protect privacy [2]. Also, discussions flag price not always matching quality, with mentions of providers like DeepInfra and comparisons to OpenRouter and groq [2].
Bottom line: demand proof, prefer transparent sourcing, and stay vigilant for AI-washing markers like vagueness about methods or real outcomes [1][2].
References
AI-Powered Is the New Cloud-Based: A Dev's Guide to Spotting Vendor Hype
Offers red flags to spot AI-washing by vendors; emphasizes explainability, outcomes, integration, proof, not marketing fluff.
View sourcedont buy the api from the website like openrouther or groq or anyother provider they reduce the qulaity of the model to make a profit . buy the api only from official website or run the model in locally
Discusses LLM provider quality, quantization (FP8/FP4), OpenRouter vs official APIs, local hosting, benchmarks, tool calls, vendor reliability and privacy concerns.
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