Show HN: DeepSeek My User Agent
A playful web app that sends browser user-agent data to DeepSeek R1 for AI-generated “roasts” is impressing many with how sharp, specific and often genuinely funny its insults are, thanks in part to visible chain-of-thought reasoning. Commenters use it as a springboard to examine DeepSeek’s unusually low pricing, model architecture, and hosting advantages, as well as long-standing quirks and privacy issues around user-agent strings and fingerprinting. Behind the humor, some see this as an early glimpse of more serious uses for reasoning models as personal filters or “buffers” against ads and low‑value web content, while others remain skeptical about accuracy and the risk of AI becoming a more manipulative advertising layer.
Project behavior
- Site takes browser headers (user agent, referrer, location, basic device info) and sends them to DeepSeek R1 to generate a three‑sentence roast.
- Prompt is visible and can be reused with other models; responses often include visible chain‑of‑thought reasoning.
- Many users paste their own roasts, noting how the model picks a few “unusual” fields (location, CPU cores, resolution, referrer) and builds jokes around them.
Humor quality and reasoning
- Many find the roasts “shockingly” funny, varied, and specific, sometimes the first time an LLM has made them genuinely laugh.
- Others see it as wordy or generic insult comedy, with reasoning text that feels like verbose self‑talk rather than deep analysis.
- Users note occasional mismatches between the reasoning and final roast (e.g., it re-selects features mid-way or repeats itself).
- Some propose comedy benchmarks and see this as evidence that humor may require careful prompt engineering.
Technical and pricing discussion (DeepSeek vs others)
- DeepSeek’s API is reported as much cheaper than OpenAI’s o1; some wonder how it can be so low.
- Explanations offered: mixture‑of‑experts architecture with only ~37B active parameters at inference, highly optimized serving on H800s, large batch sizes, speculative decoding.
- There is skepticism that US providers can easily match the price even with open weights because of engineering effort and hardware mismatch (H100 vs H800).
- Comparisons are made to Groq and other hosts; DeepSeek’s own hosting is said to be cheaper than third‑party runners.
User agent quirks, accuracy, and privacy
- Multiple comments explain that Chrome, Safari, and Firefox now freeze macOS at “10.15” in the UA string, causing the model to think Catalina is still in use.
- iPad Safari self-identifies as macOS; Chrome on Android reports simplified strings like “Android 10, K”; some systems misreport memory or cores due to anti‑fingerprinting limits.
- As a result, many roasts get OS, device model, location, or core counts wrong; some users spoof ancient browsers for fun.
- Privacy tools (Tor, VPNs, privacy extensions, GrapheneOS, DDG Browser) further confuse detection, sometimes to users’ satisfaction.
Reliability and deployment issues
- Some users see only partial reasoning or timeouts. The author later attributes this to default Vercel function timeouts and to DeepSeek API flakiness.
- When DeepSeek’s platform has outages, the page is adjusted to immediately show the prompt so users can paste it into DeepSeek’s chat manually.
Broader LLM reflections
- One subthread argues that this kind of demo is fun but trivial compared to potential “internet buffer” tools: AI layers that block ads, filter clickbait, and curate content.
- A commenter says they’re already building such a system and using it as a personal interface to the web; others express strong interest.
- There’s debate over whether LLMs will kill targeted ads or become even more powerful advertising and manipulation channels.
- Some lament that big AI advances are funded by ad-driven companies and foresee trade‑offs if ad effectiveness declines.