I built a bot trained on my own X bookmarks and likes. Around 50,000 of them, accumulated over years...
I’m a new mom who left my white collar job to be a SAHM but planned to return when they reach kindergarten age. Everyday I spiral thinking I made the wrong “financial” choice to be a SAHM instead of advance my career, I fear my job won’t exist in 5 years, and what will my child’s future look like? I feel like my algorithm definitely makes things worse! Does anyone else think about this stuff constantly? submitted by /u/properlass [link] [留言]
Follow-up to my earlier post on learning rules vs. human fMRI. Same five conditions (BP, FA, PC, STDP, untrained), same model weights, now evaluated against macaque V1/V2 (FreemanZiemba2013, single-unit) and macaque V4/IT (MajajHong2015, multi-electrode). Main findings: Early visual alignment is qualitatively conserved across species. STDP (ρ ≈ 0.30) and PC (ρ ≈ 0.28) lead at macaque V1/V2, consistent with their position in human V1. The pattern isn't an fMRI artifact. The untrained baseline result doesn't replicate cleanly. In human fMRI, Random ≥ BP at V1. In macaque, STDP and PC pull ahead of Random (electrophysiology has enough SNR to resolve the difference fMRI can't). IT alignment scales with capacity, not learning rule. ResNet-50 (pretrained, ImageNet): ρ ≈ 0.25 at macaque IT. Custom 3-conv CNN across all learning rules: ρ = 0.07–0.14. The IT convergence from the companion paper looks like a capacity floor. Cross-species IT rankings: Kendall's τ = 0.00 (p = 1.00) but n = 5 only has power at τ = ±1.0, so this is uninformative rather than evidence of non-conservation. Limitations worth noting: V1/V2 and V4/IT come from different macaque datasets with different stimulus sets (textures vs. objects): the V2→V4 drop is confounded by this switch Stimulus control shows IT rankings are weakly inverted across stimulus sets (τ = −0.40), so cross-species IT differences may be partially stimulus-driven Companion paper: arxiv.org/abs/2604.16875 Cross-species paper: https://arxiv.org/abs/2605.22401 Code: github.com/nilsleut/cross-species-rsa Happy to discuss the stimulus confound issue or the capacity control in more detail. submitted by /u/ConfusionSpiritual19 [link] [留言]
Learning copilot for human ambition via step-by-step plans Discussion | Link
Profiling PyTorch training has an interesting measurement problem: the more you measure, the more you can change the behavior of the run itself. A simple example is torch.cuda.synchronize() . It gives cleaner timing boundaries, but it also inserts synchronization points into an otherwise asynchronous CUDA workload. An alternative is to use CUDA events around selected boundaries and read them later, so timing can be captured without forcing synchronization in the hot path. This does not replace PyTorch Profiler or Nsight, but it can work as a lightweight first pass before deeper operator-level profiling. I wrote a short technical note about this while working on an open-source PyTorch training diagnostics tool: https://medium.com/p/19adf1054bcf submitted by /u/traceml-ai [link] [留言]
submitted by /u/No-Comfortable-4920 [link] [留言]
I’m a chronic overpacker, but this backpack is my perfect match, with its huge capacity, numerous pockets, and impressive durability.
Pretty little phones with pretty big price tags.
Cisco and OpenAI are redefining enterprise engineering with Codex, helping Cisco scale AI-native development, accelerate AI Defense work, and automate defect remediation.
The company is mapping Alexandria and, soon, Arlington—right across from the power center of Washington, DC.
The feds are raising the alarm about a new category of threat.
The email instructs workers to report behavior that predates Donald Trump’s second inauguration. One employee tells WIRED it felt like a “reminder to narc on your coworkers.”
Corsair unites Stream Deck functionality with Drop keyboard design expertise on its latest Galleon SD 100.