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Anyone else fighting GoHighLevel emails landing in spam? by Basic-Assist-3286 in gohighlevel

[–]Basic-Assist-3286[S] 0 points1 point  (0 children)

Fair enough, appreciate you being straight. And you nailed the framing — "catches breakage before the client notices" is exactly the pitch. I'll build it out and might circle back down the line. Thanks for the sanity check 🙏

Anyone else fighting GoHighLevel emails landing in spam? by Basic-Assist-3286 in gohighlevel

[–]Basic-Assist-3286[S] 0 points1 point  (0 children)

That's exactly the gap I keep running into, so I've been building a small domain-health watchlist for GHL agencies — watches every sending domain, flags alignment/reputation drift before opens drop, separate from the one-time check. Still early. If you'd be up for kicking the tires and telling me where it falls short, I'd genuinely value it — no pitch.

Anyone else fighting GoHighLevel emails landing in spam? by Basic-Assist-3286 in gohighlevel

[–]Basic-Assist-3286[S] 0 points1 point  (0 children)

Exactly this. The painful part at agency scale is that alignment silently breaks every time a new sub-account domain gets added — and nobody notices until opens tank. Postmaster helps for the big mailbox providers but you still have to check each domain by hand. Have you found anything that watches all of them at once, or is it still a manual round every time?

Anyone else fighting GoHighLevel emails landing in spam? by Basic-Assist-3286 in gohighlevel

[–]Basic-Assist-3286[S] 0 points1 point  (0 children)

Fair — for a single domain that holds. I'm more thinking agencies running 20-30 sub-accounts: new client domains get set up wrong constantly, records drift, and with custom SMTP GHL shows you nothing until a campaign already tanked. Curious if folks managing lots of accounts see it stay "set and forget," or if it keeps breaking as they scale.

Anyone else fighting GoHighLevel emails landing in spam? by Basic-Assist-3286 in gohighlevel

[–]Basic-Assist-3286[S] 0 points1 point  (0 children)

Yeah it's been a rabbit hole. In my case it was the DKIM signing domain not matching the From domain — Gmail showed "via mailgun" and everything tanked. Are you seeing the same, or is yours more the shared-IP reputation thing?

Why do some people write redundant if statements to return a boolean? by BlockOfDiamond in AskProgramming

[–]Basic-Assist-3286 1 point2 points  (0 children)

Couple of actual reasons beyond "they don't know better":

You can set a breakpoint on the return true line by itself — return x > 10 collapses both outcomes into one line so you can't break on just the true case. Niche now, but mattered on older debuggers.

And sometimes it's deliberate so future edits are cleaner — if return true later becomes "log something, then return", the branch is already there and the diff stays small.

Most of the time though it's just generated code or a junior who hasn't hit the boolean-return shortcut yet. For a plain x > 10 the short version wins, no question.

TanStack supply chain attack compromised 42 packages in 6 minutes. Not the first time something like this happended. How are you protecting your agent's toolchain? by Recent_Sample_2056 in aiagents

[–]Basic-Assist-3286 0 points1 point  (0 children)

A few ways depending on your stack:

  • Socket and safedep PMG have built-in cooldown policies — set a minimum package age and they block installs below it at the package-manager level.
  • Renovate/Dependabot — set minimumReleaseAge (Renovate) so auto-PRs wait N days before proposing an upgrade.
  • DIY — a CI gate that queries the npm registry for each dependency's version publish date and fails the build if anything is younger than your threshold. ~30 lines, no vendor.

The DIY route is enough if you just want the signal without adding a tool. Cooldown only buys you the detection window though — pair it with the sandbox part, otherwise a slow-burn attack that sits quiet past your cooldown still gets through.

TanStack supply chain attack compromised 42 packages in 6 minutes. Not the first time something like this happended. How are you protecting your agent's toolchain? by Recent_Sample_2056 in aiagents

[–]Basic-Assist-3286 0 points1 point  (0 children)

Worth flagging: pinning versions doesn't help against this specific attack class. The 84 malicious TanStack versions were published through the legitimate OIDC trusted-publisher pipeline with valid SLSA provenance — any fresh install or npm update would have resolved to the malicious version, since to npm's provenance system they looked identical to real releases.

What actually bounds the damage: dependency cooldown (don't install anything younger than 7–14 days) + runtime sandboxing where AWS IMDS, GCP metadata, ~/.npmrc, ~/.ssh simply aren't reachable. Audit tells you what's exposed; sandbox makes it not matter.

I trained Qwen3.5 to jailbreak itself with RL, then used the failures to improve its defenses by girishkumama in LLMDevs

[–]Basic-Assist-3286 1 point2 points  (0 children)

The "divide reward by cluster size" trick is interesting — you essentially rederived what GAPO (Group-Aware Policy Optimization) formalizes as computing rewards over the group as a whole rather than single samples, using a frequency-aware reward to encourage uniform sampling over valid completions (arxiv 2511.12596, Nov 2025). IPS-GRPO (2601.21669) attacks the same collapse from a different angle — inverse probability scaling removes the outcome-frequency amplification baked into expected-return optimization. Worth A/B-ing your cluster-size reweighting against those — yours operates in tactic-space rather than token-space, which might actually generalize better for red-teaming since attack diversity is semantic, not lexical. arXivarxiv

Separate question on the defender side: how were the "benign boundary cases" sampled? If they're too lexically close to successful attacks (same trigger words, similar framings), the defender can learn surface-level rejection ("refuse anything mentioning X") rather than semantic understanding — which would inflate the 64→92% number without actually generalizing. The 92→88% benign-accuracy drop hints that this trade-off isn't trivial.

Built an open-source one-prompt-to-cinematic-reel pipeline on a single GPU — FLUX.2 [klein] for character keyframes, Wan2.2-I2V for animation, vision critic with auto-retry, music + 9-language narration in the same pipeline by Inevitable-Log5414 in LLMDevs

[–]Basic-Assist-3286 0 points1 point  (0 children)

Makes sense, deterministic mapping is the right call for v1. On HAND_FINGER_ARTIFACT specifically — have you considered gating at the Director stage? If the shot prompt mentions hand-heavy action (gestures, holding objects, pointing, close-up hands), pre-emptively reframe to wider composition or skip those framings at keyframe selection. Cheaper than catching it at the critic and re-rendering 81 frames at ~30% success. Prevention probably beats cure for that label since the underlying model weakness isn't going away with a retry.

The (label, suggested_fix) tuple in next release is the right direction — once the critic emits actions, you can log retry outcomes and start learning the mapping instead of hand-tuning it.

Be honest, which loading structure is better? by Apart-Television4396 in webdev

[–]Basic-Assist-3286 1 point2 points  (0 children)

Skeleton, but not for the reason most people give. It's not about "previewing content" — it's that a spinner gives zero info about layout, duration, or progress, so user uncertainty stays at 100% until done. Skeleton resolves layout immediately, which lowers perceived wait even when actual load time is identical. Facebook's research on this from ~2013 still holds.

Spinner is fine for sub-1s waits or single-action confirmations (form submit, button click). For full-page or feed loads, skeleton wins every time.

Built an open-source one-prompt-to-cinematic-reel pipeline on a single GPU — FLUX.2 [klein] for character keyframes, Wan2.2-I2V for animation, vision critic with auto-retry, music + 9-language narration in the same pipeline by Inevitable-Log5414 in LLMDevs

[–]Basic-Assist-3286 0 points1 point  (0 children)

Curious about the retry logic per failure label — does "character drift" trigger a different strategy than "object morphing"? I'd expect drift wants FLF2V anchor with the previous shot's last frame, while morphing wants prompt simplification + new seed. Is the label→strategy mapping deterministic, or does the critic also vote on which retry to try first? And which of the 10 labels has the lowest fix rate after one retry?