deadpush is an always-on guardian for that failure mode.

pip install deadpush
deadpush protect --hardened

The problem it targets

When agents run with broad filesystem and git access, common failure modes include:

• Hardcoded secrets and “temporary” credentials
• LLM context / rule files committed by accident
• Burst writes that create debris faster than you can review
• Local hooks that a determined agent can bypass unless you also enforce server-side

What deadpush does

• Realtime watch — monitors the repo and quarantines dangerous writes
• Same enforcement kernel across daemon, git hooks, and MCP paths
• Tiers — local harden/sandbox modes, plus CI / pre-receive options so violations cannot merge
• Safety score — reacts when multiple agents go wild in parallel

Details and threat model live in the repo docs.

Who it is for

• Engineers running Cursor / Claude / other agents overnight
• Teams that want a required status check for secret/debris scans
• Anyone who has cleaned up an agent-authored commit they did not intend to ship

Links

• GitHub: harris-ahmad/deadpush
• Install: pip install deadpush · PyPI
• Related tools: gitpull, ChatLiberate

If you try it and hit a false positive worth teaching the guardian, open an issue or email me.

In our ACM Web Conference 2024 (WWW ‘24) paper, we measured the hidden data cost of in-stream YouTube video ads from the user’s perspective — not advertiser CPM, and not platform revenue.

Paper: Uncovering the Hidden Data Costs of Mobile YouTube Video Ads (PDF, code & data)

What we measured

We built a crawl/stream pipeline and released a public corpus covering:

• 17,600 main videos
• 46,600+ in-stream ad videos
• ~8,225 hours of streamed content
• 8 countries (four developing, four developed)

We then analyzed client buffer state around ad placement — which is where latent waste shows up.

Key findings (short version)

1. Skipping is not free. Users frequently still download 80–100% of a skippable ad’s data after skipping (~31% of cases in developing regions, ~52.6% in developed regions in our data).
2. Mid-roll ads disrupt the main video buffer. A mid-roll can force re-download of as much as ~71 seconds of main-video on average early in playback.
3. Affordability impact is real. Excess losses averaged about 6.7% of a 2GB mobile plan in our affordability analysis.

Put differently: ad playback policies interact with buffering in ways that charge users for bytes they never asked to consume.

Why this matters for builders

If you work on video players, ABR, ad insertion, or mobile products in emerging markets:

• Treat skip as a hard stop on further ad media fetch when possible.
• Avoid policies that discard large main-video buffers on mid-roll insert.
• Measure user-paid bytes, not only completion rate or fill rate.
• Publish buffer/ad interaction metrics the way we publish latency SLOs.

What we released

The dataset and toolchain are public so others can reproduce and extend the measurement:

• Paper PDF
• Code & data on GitHub
• DOI

Citation

Emaan Atique, Saad Sher Alam, Harris Ahmad, Ihsan Ayyub Qazi, Zafar Ayyub Qazi. Uncovering the Hidden Data Costs of Mobile YouTube Video Ads. WWW ‘24.

If you’re studying video affordability, ABR/ad interaction, or mobile QoE in developing regions and want to compare notes, email me.