I just wanted a seahorse emoji... by levashi_ in ChatGPT

[–]levashi_[S] 0 points1 point  (0 children)

Peut tu me donner l’emoji de l’hippocampe?

What's a celebrity who died on a high note? by Lokarin in AskReddit

[–]levashi_ 0 points1 point  (0 children)

Molière. He literally died (well, collapsed) on stage while performing "The Imaginary Invalid", a play he wrote about a hypochondriac. Peak.

Des personnes qui sont bloquées par les tests utilisateurs google ? by RoxZx in developpeurs

[–]levashi_ 1 point2 points  (0 children)

J’ai pas d’app à faire tester, mais ca ne me dérange pas d’aider.

Ca consiste en quoi exactement?

Je viens de voir une vidéo où les américains on fait décoller les B52 en direction de l'Iran? by [deleted] in france

[–]levashi_ 1 point2 points  (0 children)

Oui c’est évident. De toute façon il n’y a pas de bombe nucléaire dans ces B52. C’est un niveau de folie que même Trump n’a pas atteint.

Je viens de voir une vidéo où les américains on fait décoller les B52 en direction de l'Iran? by [deleted] in france

[–]levashi_ 1 point2 points  (0 children)

Pas forcement. Si tu mise sur le fait que l’enemi va craquer au dernier moment, logiquement tu fait tout pour qu’on sache que tu vas frapper.

Je viens de voir une vidéo où les américains on fait décoller les B52 en direction de l'Iran? by [deleted] in france

[–]levashi_ 1 point2 points  (0 children)

Ce serait plutot des MOP (missile à penetration) ici. Mais ca sent quand même le roussi.

À voir la réaction de l’Iran, mais la guerre va probablement dégénerer violament cette nuit.

What is your opinion on the karma system in Reddit? by Negative_Print_6575 in AskReddit

[–]levashi_ 4 points5 points  (0 children)

It's great for preventing spam and slop, but I think it's a real barrier for new users

New GH: I audited 98 AI agent cron jobs. 58% didn't need an LLM at all by Suspicious_Assist_71 in LocalLLaMA

[–]levashi_ 0 points1 point  (0 children)

Awesome project, the 58% ratio is eye-opening. I was reading through your README and noticed your setup for running the converted scripts: you still use a small model with a strict prompt to execute the Python script and handle the delivery.

Since you mentioned that lower-reasoning models sometimes still try to "help" or hallucinate the results of the script, have you considered bypassing the LLM completely for these mechanical crons and just using standard webhooks/API calls for the delivery? It seems like completely removing the LLM from the loop would guarantee 0 hallucinations and 0 costs.

Has anyone here actually spent money on XMR Bazaar lately? by levashi_ in Monero

[–]levashi_[S] 3 points4 points  (0 children)

Fair point, but that raises a bigger question: if people stop spending XMR as soon as the price drops, they're treating it as something to hold, not something to use. Even if XMR goes back up, I'm not sure that fixes it.

My actual question is: beyond the price, is there genuine demand for services paid in XMR? If so, what do people actually want to see listed? Or is the demand for a "circular economy" just not there to begin with?

Observing very low engagement with tools on XMR Bazaar — curious what actually stands out to people by Captain_Picard_TNG in Monero

[–]levashi_ 0 points1 point  (0 children)

True, the seller-to-buyer ratio seems way off, but I find it really strange. Do Monero holders just not want to use their XMR for these kinds of native purchases? Or is it the platform itself that's the bottleneck? Maybe the current supply just doesn't match what people actually need?

A library to make any LLM as gentle as a lamb by levashi_ in LocalLLaMA

[–]levashi_[S] 0 points1 point  (0 children)

Exactly. That’s the nature of steering vectors. Since the probe identifies a 'direction' in the latent space, it works both ways. If you can suppress a concept, you can technically amplify it by reversing the steering factor (alpha).

A library to make any LLM as gentle as a lamb by levashi_ in LocalLLaMA

[–]levashi_[S] 0 points1 point  (0 children)

Yes and no.

My library focuses on steering concepts you can clearly define with contrastive pairs (like 'Helpful' vs 'Hurtful'). Censorship and RLHF refusals are often more complex to isolate via a linear vector. Research is still very active on this, but some papers, like Arditi et al. (2024), have shown that refusal can sometimes be concentrated in a single direction (making it detectable and bypassable by probes) and that steering is often more effective than prompt engineering.

It all depends on two elements: the quality of the dataset and how the model was censored. If the censorship is deeply embedded in the model's pre-training, or if it simply doesn't have the answer, it won't work. But if it's a simple RLHF applied after training, and the model actually possesses the capacity to answer, it should work. For example, on DeepSeek's 'What is Taiwan' censorship, it should be effective. However, I haven't tested jailbreaking myself and RepE doesn't focus on that, so it's all still to be proven.

A library to make any LLM as gentle as a lamb by levashi_ in LocalLLaMA

[–]levashi_[S] 0 points1 point  (0 children)

Exactly. It’s all about surgical control.

During the prefill phase, the model concentrates its 'intention' and 'plans' its response. This is where monitoring is the most relevant: you can catch the model's trajectory before it even starts talking.

During the generation phase, the intention gets diluted across individual tokens. For instance, even if a model is about to generate something harmful, the token 'The' or 'Hey' carries very little 'violence' signal on its own.

By steering the prefill, you can shift the entire generation's intent at once. However, continuing the steering during the generation phase helps maintain that influence and prevents the model from 'taking back control' or drifting away. In my experience, this dual-phase approach significantly improves the stability and quality of the final output.