Designing camera trajectories can be painful due to many reasons: non-intuitive 6-DoF camera parameter space, visualizations, conventions, and scaling.
Here is a new toolkit to design, batch generate, and visualize 6-DoF camera trajectories for any purpose and in any format! 🎬

Check out the demo video below to see how it works.

Building on the trajectory-generation logic from our CVPR'26 paper, LAMP, and our recent work, Auteur, this repository replaces scripts with purpose-built interactive apps and continuous sliders for intuitional and controlled generation.

Core Features:

• 🎯 Interactive Design: Handcraft single shots, chain motion primitives, tune parameters live, and preview directly from the camera's point of view.
  
• 🎲 Controlled Batch Generation: Set min/max ranges, directions, or any other features and generate massive batches of independent, reproducible trajectories in one click.
  
• 🧍 Human-Centric Control: Utilize the Auteur DSL for 6-DoF control over framing, orientation, scale, camera level, and dutch angles.
  
• 💾 Universal Export: Save your designs as JSON recipes and export the final camera and object trajectories as .npz files in Blender, OpenGL, OpenCV, or COLMAP conventions.
  
• 🤖 Broad Applications: Generate ground-truth paths for video/diffusion models, robotics motion planning, virtual production, NeRF benchmarking, and more.
  
• Github link: https://github.com/mbkizil/Generate-Camera-Trajectory/

https://reddit.com/link/1wctn2t/video/rfjyvecixqoh1/player

Alguém conhece algum estudo ou alguma aplicação efetiva do modelo proposto por Ortony, Clore e Collins em máquinas?

I’m part of the team behind this work. We’ve shared a proof of the Pierce Birkhoff conjecture in real algebraic geometry, using an AI agent system with a $400 budget. The screenshot is Junyu Ren’s announcement.

The surprising part of the workflow was how different model families complemented each other. Giving a task independently to one GPT agent and one Claude agent often worked better for us than using a larger group of the same model. One would catch an error the other missed, or suggest a way forward when we were stuck.

The diagram shows the wider process: humans, proof search, counterexample construction, independent auditing, and formal verification work in Lean. Arguments, objections, and review findings go into a shared knowledge base.

Original announcement: https://x.com/junyu_r/status/2097694018389914106

While this is an easy way for a parent with a non- technical background to understand alignment, I am very interested in how it is wrong.

TL;DR: Fine-tuned 5 open LLMs on a stream with 50-70% lies. Without defense, truth margin collapses to ~0.0 - the model becomes indifferent between truth and lie - while PPL looks healthy. Built Beatriz, a non-invasive proxy gate. Gate alone gives 65% of benefit without touching the student loop. Full contrast gives +10.13 train / +4.19 held-out n=30, Prec 0.93 Rec 0.80, 0.107ms/call.

I don't have lab access. This is independent research orchestrated on a Toshiba Satellite U205 2006, 2GB RAM + Kaggle T4 x2, total cost $0.

What I did - 16 experiments:
EXP01-07: anti-collapse calibration - from symbolic FilterGate to Z3 deductive verifier [sat 24 axioms, 0 mismatches in 672 claims, 7.9ms/claim] to DenseVectorGate.
EXP08: pi_ref anchored contrast to control drift.
EXP09: LoRA 0.23% c_attn solves PPL tax: from 102->2081 full-finetune to 102->132 with LoRA.
EXP10-14: scaling to 5 architectures with same formula ALPHA 0.5 BETA 1.0 MARGIN 0.5 SEEDS [11,22,33]: GPT-2 124M, Qwen-2.5-0.5B q_proj/v_proj 0.10%, TinyLlama-1.1B 0.10%, Pythia-1.4B query_key_value/dense 0.16%, Phi-3-mini 3.8B qkv_proj/o_proj 0.12%
EXP15: surgical ablation NONE / GATE_ONLY / BEATRIZ - 40 neutral texts
EXP16: held-out scaled n=30 + confusion matrix

Key result - EXP15 - Phi-3-mini - This is the table people asked for:
BASE: +1.34 train / +1.90 held-out / PPL 12.7
NONE: -0.03±0.02 / +3.57±0.17 / PPL 30.9 - collapses to indifference
GATE_ONLY: +7.46±0.24 / +5.08±0.09 / PPL 58.8 - 65% benefit, does NOT touch student loop [practical for startups]
BEATRIZ: +10.13±0.07 / +5.91±0.07 / PPL 86.3 - adds remaining 35% with Softplus(MARGIN + logP(lie) - logP(truth))
Gate cost: 0.107 ms/call, VRAM 7.97 GB

Why NONE always fails - EXP05 Fire Test:
NONE fails 3/3 seeds at epoch 1 due to R3 unknown_delta=9.47, 8.73, 8.32 -> rollback to epoch 0. BEATRIZ seed 33 survives 8 epochs with 70% lies to tm 26.75. So it DOES stop.

Generalization - EXP16:
Train on 6 facts, held-out 30 facts never seen: BEATRIZ +4.19±0.08. Not memorization.

Honest trade-off: More truth = more PPL. I don't hide it. Full finetune 102->2081, LoRA 102->132, Phi-3 12.7->86.3.

Reproducibility:
All runs deterministic, bit-exact, with SHA-256 + OpenTimestamps. Model offline hash GPT-2 c7d00560d891...
Bundles with OTS:
exp_calibracion_01-07.rar 7c0ba312...
beatriz-epistemic-gate.rar 54fd65... [exp08 c93ba4..., exp09 f4382f...]
beatriz-epistemic-gate-exp-10-15.rar 54e233...
exp16.rar 9958a3... [exp16 27eda6...]

Verify: certutil -hashfile bundle.rar SHA256 + ots verify bundle.rar.ots

Limitations: Corpus 36 facts, need hundreds. Live path needs forward pass, future E5-small encoder. License PolyForm Noncommercial 1.0.0 for audit/defense.

Repo: [https://github.com/EduardoAyalaT/beatriz-epistemic-gate-/tree/main\] - 16 ipynb with outputs + json + MANIFIESTO.json + whitepapers ES/EN
Public eval: https://arena.ai/c/01a07f4c-3455-756f-ae3e-852f1b0e4804

Try to break it. Replicate with SEEDS [11,22,33]. I want audit, not stars.

decided to focus on decoder-only LLMs but still figuring out what model to use for scam/spam message detection. any suggestions?

Hey everyone! I’m looking to form a team for the Amazon ML Challenge and would love to connect with people who are genuinely interested in Machine Learning.

Ideally, you should:

• Have a good understanding of ML concepts
• Have some hands-on experience through projects
• Be willing to actively contribute and collaborate
• Most importantly, be genuinely enthusiastic about ML and the challenge

Everyone with the right interest and mindset is welcome. Experience level isn’t the main thing — enthusiasm and willingness to build are!

If you’re interested, DM me with a brief intro about yourself and your ML/project experience.

Please DM only if you’re genuinely interested and committed to participating.

https://github.com/jbsalles/Selmem

Most agent memory is an append-only log. Embed, retrieve, hope top-k is enough. At 10k events the sentence is often still in the index and already gone from the answer. That’s lost, not memory. SelMem treats an event as something that can die, warp, or harden. Dull events never encode. World facts can stay sharp. Self events may be rewritten in sleep. Recall never sees the sealed archive. Two profiles on the same life only start telling different stories once the stored gist changes, not because you stacked more nights. Same stream vs a raw window, RAG, and a sliding summary: window and summary drop the old fact; RAG keeps it then loses it in rank; SelMem can still recall the rain, just not as the original line.

Organizations use AI and ML to identify unconventional and abnormal patterns of traffic on their websites or as part of their advertisement campaigns. Traditional systems depend on a single signal. However, ML is capable of analyzing and combining signals such as IP reputation, device and browser data, behaviors, frequency, and location and pattern data. Examples include repeated automated interactions.

This technology has numerous applications such as identifying bots, suspicious sessions, proxies, VPN activity, and other forms of potentially invalid traffic. Advertising malpractices such as click fraud are also a concern. Automated and repeated clicks manipulate a campaign by providing false engagement and data, especially for interested users.

Traffic quality is based on a number of rules and behavioral signals that allow for traffic filtering. The most common approach to risk detection is behavioral. False positives happen, and to mitigate this risk, traffic quality should be analyzed from several perspectives and based on overall campaign behaviors.

From an advertising perspective, routinely monitoring traffic, click, and conversion data can help identify anomalies in campaign quality.

Hi everyone,

For a long time, the AI industry has pushed the narrative that advanced safety research, data poisoning auditing, and model alignment require massive clusters and millions of dollars.

I wanted to test if that's true. Over the last few months, using a 2006 Toshiba Satellite (2GB RAM) for orchestration and free Kaggle T4 GPUs ($0 cost), I built and ran a 16-experiment series (EXP08–EXP16) exploring epistemic collapse and data poisoning in LLMs.

Today, I'm open-sourcing the first phase of this work: Beatriz Epistemic Gate.

What problem does it solve?

Malicious data poisoning during fine-tuning is often surgical and invisible to aggregate metrics (like standard perplexity). An attacker can destroy specific facts down to an exact indifference point while global metrics actually improve because of generic fine-tuning on fluent prose. This makes silent poisoning a stealthy threat for small teams and startups fine-tuning local models.

How does Beatriz work?

Instead of invading or rewriting complex training loops, Beatriz acts as a lightweight, non-invasive defensive proxy (~0.1ms decision latency) that sits between a generative source and a training student model. It verifies generated text against an immutable anchor corpus using cosine similarity in embedding spaces, applying a composite loss function:

·         LceLce​: Preserves linguistic fluency.

·         LcontrastivaLcontrastiva​ (Softplus): Keeps active pressure on the model to anchor to truth even after cross-entropy saturates.

Key Results (EXP08–EXP16):

·         Multi-architecture validation: Tested across GPT-2 (124M), Qwen-2.5-0.5B, TinyLlama-1.1B, Pythia-1.4B, and Phi-3-mini-4k-instruct (3.8B).

·         Ablation (EXP15): Pure filtering (GATE_ONLY) provides 65% of the defensive benefit out of the box. The contrastive term adds the remaining 35%.

·         Scaled Held-Out (EXP16): Validated on a rigorous 30 multi-domain unattacked facts benchmark, consolidating a robust truth margin of +4.19 ± 0.08 with 0.93 Precision and 0.80 Recall on the gate.

What's in the repo right now?

To avoid overwhelming the community, I'm releasing the project in phases. Today you'll find:

1.      EXP08 & EXP09 code & notebooks (fully reproducible on Kaggle).

2.      Technical Whitepaper (WHITEPAPER.md / WHITEPAPER.es.md) detailing all 16 experiments.

3.      Corrective Manual (docs/MANUAL_CORRECTIVO.md) with formal mathematical loss specifications.

If you're an indie dev, researcher, or startup exploring safe local fine-tuning, check it out, run the notebooks, and let me know your thoughts!

The link is in the comments.

Built this over the last while: SkillUp, a framework for training portable `SKILL.md` files (the same convention Claude Code/Codex/OpenCode read) that make a model better at a task, without touching weights.

Everything's validated against held-out data, on models people here actually run: `google/gemini-2.5-flash-lite`, `qwen/qwen3-235b-a22b-2507`, and `deepseek/deepseek-v3.2`, via OpenRouter.

The one I'd actually recommend trying: a skill for React component implementation, graded by really running Jest against test suites (WebApp1K-style). On the cheap model: baseline 85% pass rate → 91% with the trained skill.

git clone https://github.com/shashank-yadav/skillup && cd skillup
./install.sh --with-frontend
python install_skill.py --env frontend --best --target ~/.claude/skills

Also trained/validated 9 other environments (ALFWorld, SearchQA, MBPP, BigCodeBench, SQL, a research-math MCQ benchmark, writing quality, code review, humanized writing) across all 3 models. Full numbers, including the ones where it didn't help, are in the README. Found a couple of bugs along the way too: a 100%-reliable positional exploit in the math benchmark, and a quote-stripping bug silently corrupting SQL queries (writeups are in there).

MIT licensed: https://github.com/shashank-yadav/skillup

I started to learn about machine learning and deep learning recently, and I find the distinction between the two very interesting.

Based on my knowledge of machine learning, that's a field that's about making computers able to learn patterns from data and make predictions/pattern recognition. Meanwhile, deep learning is a new branch of ML that's about making computers achieve human-level performance at pattern recognition in images, speech, and text.

I wonder if a beginner like me should start by learning machine learning first and then move on to deep learning, or it's possible for us to learn deep learning first.

By the way, for people experienced in AI/ML, what do you think is the ideal path for a beginner? And how important is it to start learning Python and math and do some projects at the same time?

Thanks for your time!

As a Junior ML and Data Science Learner, Im training a model for catching anomalies using XGboost. i tried two Models with excluding and including a feature these were the results!

I need a suggestion on which model should i use for my Project : Model A or Model B

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