Researcher Routes Home Traffic Through Chosen ISP Upstream Without Router Access

·1 views

A developer conducted a self-described Computer Science experiment to determine whether an end user can influence which upstream provider carries their internet traffic, without accessing the ISP's routers. By studying BGP Best Path Selection, they identified that AS-PATH length and Local Preference are the key factors an ISP uses to choose between upstream providers. The researcher used bgp.tools to confirm their ISP had two upstreams — TransTeleCom (Russia) and RETN Limited (UK) — then searched for a VPS hosted on a subnet routed through RETN's infrastructure. After selecting an EDIS Global server in Tallinn, Estonia, and routing traffic through a VPN tunnel, the researcher used the Trippy tool to monitor packet paths during a 15 GB Steam download. The route remained entirely stable through RETN throughout the test, partially confirming that targeted upstream selection is achievable by ordinary users through careful VPS and VPN configuration.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Open Letter Urges Anthropic CEO Dario Amodei to Release Claude Model Weights

A public open letter addressed to Anthropic CEO Dario Amodei has been published, calling on him to release the weights of the company's Claude AI models. The author argues that if Amodei genuinely believes in the principles he publicly espouses around AI safety and openness, he should back those words with action by open-sourcing the weights. The letter has gained traction on Hacker News, accumulating points and sparking discussion in the AI community. The debate reflects a broader ongoing tension between open-source AI advocates and companies that keep their model weights proprietary for safety or competitive reasons.

0 comments Read more at Hacker News

ProgrammingDEV Community ·

Screenshots Fall Short as Agent Review Artifacts, Structured Evidence Bundles Offer Fix

Screenshots shared in pull requests for AI agent runs lack critical context such as trace provenance, redaction details, and tamper evidence, making meaningful code review difficult. A more reliable alternative is a derived, redacted evidence package that includes structured files like HTML reports, JSON manifests, redaction reports, and hashed trace copies. The AgentInspect Evidence v2 bundle implements this pattern by separating source hashes from packaged-file hashes, ensuring reviewers can verify integrity without accessing private source traces. An offline verifier checks the manifest structure, file hashes, and provenance, flagging any post-finalization edits or unexpected files. This approach gives reviewers a bounded, inspectable artifact while keeping the original trace data private to the developer's machine.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

AI-Generated Websites Accused of Looking Cheap and Identical, Like 90s Word Art

Frontend developers and designers are drawing comparisons between today's AI-generated websites and the low-effort Word Art aesthetic of the 1990s, arguing that users instinctively recognise AI designs as generic and cheap. A 2025 study found that over 80% of AI-generated websites share nearly identical underlying structures, as these tools largely replicate existing internet trends rather than producing original work. Tools like Framer's Wireframer can produce responsive layouts in under 60 seconds, but critics say the results look assembled from outdated SaaS design templates. Industry observers argue that AI primarily threatens the mid-tier template market, not skilled designers, since strategic thinking, branding, and SEO remain beyond its current capabilities. Agencies also warn that rebuilding poorly performing AI-generated sites often ends up costing more than hiring an experienced designer from the outset.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How a 1994 Compression Algorithm Became the Backbone of LLM Tokenization

Modern large language models rely on a tokenization method rooted in Byte Pair Encoding (BPE), a data-compression algorithm originally developed by programmer Philip Gage in 1994. Tokenization is the process of breaking text into smaller units called tokens before a model processes them, and the way this split occurs affects input length, inference cost, vocabulary size, and multilingual performance. BPE works by repeatedly merging the most frequently co-occurring byte sequences in a training corpus, striking a balance between character-level and word-level approaches. Unlike word tokenizers, BPE can handle rare or invented words by decomposing them into known sub-units, without requiring an infinitely large vocabulary. The algorithm gained widespread adoption in neural machine translation before becoming central to tokenizers used in models like GPT-2 and its successors.

0 comments Read more at DEV Community