... spent way too much time this week looking for an old client payment record across messy folders. Since my freelance work is pretty sparse paying $20/month for a subscription based accounting app feels like overkill. But sticking to basic spreadsheet templates is clearly getting disorganized as time goes on. There has to be a middle ground between manual sheets and heavy SaaS apps. How do you handle client billing when your workload is light and doesn't warrant a full ...

... could have. And I still don't have a clean answer on the verification-method question, because the honest version is that I don't yet know what I'd do differently with a government-ID-checked 16 versus a self-declared one. I'd rather say that than write a paragraph pretending I've resolved it. The thing I am confident about is narrower. The signal is sparse

... exists in order not to do. One more, and it is a correction. In update #2 I wrote that values gives you "exactly the schema you asked for and nothing else". That was not true when the schema was sparse. I declared a single field on a delivery note and got four more back that I never asked for, with a declared count of 44. The thinner the schema, the more the model added whatever else it could ...

... paper argues that they still lack the abductive jump needed to originate new foundational hypotheses. Given Einstein's equivalence principle, a model might derive much of the later mathematics. The harder step is inventing that principle when observations are sparse. Why this matters This is not an argument that AI cannot contribute to research. It is a warning that automated proof, experimentation, and optimization are not the same as genuine scientific invention. The proposed path forward is not only larger ...

... Parameter count alone has become a bad shortcut for explaining conditional models. If a system uses experts or sparse memory, I want four numbers: total parameters, parameters active per token, routing or retrieval overhead, and measured throughput on named hardware. Without those, a large number can describe storage capacity while readers assume every parameter contributes to every token. That muddles both cost and capability. Which number do you wish model announcements reported by default ...

... away from stripe, it's the same parent. the roadmap's gone pretty quiet there too. if the whole point is not having stripe as a single point of failure, polar or creem or paddle are the real alternatives. paddle is probably overkill until past ~$10k mrr, it's built for scale rather than launch. Comment from Matthias Str lman(@matthias_stralman): @busmark_w_nika  yes, the stripe ban horror stories are real, and that is exactly the case where ... ... Comment from Konstantin Tikhaev(@konstantin_tikhaev): @busmark_w_nika  Inspect test files in every diff. Agent changed expected output not logic, it "cheated" ) Comment from Sabber Ahamed(@sabber_ahamed): @busmark_w_nika   @abdullah_javaid3  the invitation vs

... Hugging Face. That freedom lets us explore efficient architectures larger labs may consider too experimental. With Metis-1.6, we re also preparing a paper on our new MoRE architecture, Mixture of Recursive Experts. Without getting too technical, MoRE combines sparse expert selection with recursive computation. The goal is to give the model substantially more reasoning capability than its raw parameter count would suggest while keeping inference fast and inexpensive because only a fraction of the model is active at once ...

... Comment Deleted Comment from Rahil Pirani(@rahilpirani): @djbutterrock_goulet   That's what the ranking is for. Direct matches always score above connected memories, so the right answer comes first. The graph adds context when direct evidence is sparse - it doesn't pile on top of a clear answer. Comment from Tai Tran(@tai_tran4): For a default setting, I d lean towards #2 (Direct matches plus one connection). As a founder, I usually just need the immediate answer ...

... assistant set and update a chart s description when editing it. Added hover tooltips, a crosshair, and clearer legend and segment interactions to the AI usage charts in billing settings. Fixed vertical bars overlapping on time-series charts with sparse, clustered dates. Fixed setup for Fivetran file connectors like Google Drive so Magic Folder connectors create correctly. Made chat handle files that can no longer be retrieved more gracefully instead of surfacing a generic error. Comment from Rahul Bhavsar(@rahulbhavsar ...

... achieves  71.55% zero-shot top-1 accuracy at 84.0% token sparsity  on SigLIP, retaining 92.4% of dense accuracy without modifying any weights. Architecture B (Extreme Wall-Clock Speedup):  Physically eliminates stationary tokens before the encoder. With sparse manifold distillation, it reduces 1792p SigLIP 2 inference from 678 ms to 11.9 ms a  55.80 wall-clock speedup  at 97.37% embedding fidelity. LLM Ablation:  Characterises the architectural boundaries of applying similarity-gated bypass to autoregressive language ...

... intel with time-based context - the kind of specificity I desperately wanted when planning trips but couldn't find anywhere. Known issues: UI is rough, mobile experience needs work, data coverage is limited to two cities, some neighborhoods have sparse data. This is very much beta. What I'm looking for: Feedback on usefulness vs

... role or company on LinkedIn. The platform gives you a lot of algorithmic juice around that moment, so if you re strategic you can get a lot of visibility from it. Early on, this stuff matters, those sparse likes and comments give you some social signal. 3. LinkedIn We re a meeting software tool, so we ve pushed hard on LinkedIn. It makes sense for our category. I ve found personal channels almost always outperform the brand account ... ... Comment from Ashir Murtaza(@ashir_murtaza1): The point about personal profiles outperforming brand accounts is so true, especially on LinkedIn. People connect with people, not logos. Comment from Johanna John(@johanna_john1): Loved the honestly about newsletter traffic vs

... along with making coding easier/accessible for everyone). And currently, I'm the guy who likes to move fast. I'll sign up for a time slot! Comment from Vihaan Kodiganti(@vsync_66): Hi... I'm building LeoIgnite AI, an alternative to generic mental health apps/chatbots. For the app, I've made it web based. Particularly through Lovable's natural language app builder (aka vibe-coding). I'm running the website on a Supabase backend, with Gemini as my primary ... ... with PayPal because I already had the account laying around. Might add Stripe later. I just went with a pay as you go credit based system and I just track the credits in my own DB. Tools/Process: Fired up VS

... search against user queries, pass top matches to an LLM for relevance filtering. It worked good, but not great. Here's why: Problem 1: Facts don't embed well "User streams on Tuesdays" or "Audience is mostly Brazilian" are sparse semantic content. Embeddings need richer context to match meaningfully. Problem 2: Every query required an LLM pass Just to filter results. Expensive and slow. Problem 3: Reducing everything to facts loses information Tweets, conversations, uploaded docs all flattened into ...

... handle hybrid recall or scale gracefully. Enterprise teams need auditability + efficiency, not black-box memory. What GraphBit s Data Layer does differently Unified Retrieval One interface across pgvector, Qdrant, Weaviate, Elastic. No lock-in. Hybrid Search Dense + sparse + graph edges for accuracy & fewer hallucinations. Versioned Memory Short-term, long-term & time-scoped recall with audit logs. Targeted Caching Hot responses + tool I/O caching to cut infra spend. Real-time Ingest CDC streams keep data fresh within seconds. Privacy ... ... doesn t just orchestrate- it scales, predictably. Would love your feedback: What s the biggest data pain you ve faced in scaling AI agents? Try it here: @GraphBit Musa Comment from Musa Molla(@musa_molla): @imrul_kayes10  Freshness vs

... affiliate content to boost revenue, right? Good point. Although we see monetization as a key hook for early adopters struggling to generate content they can monetize. I could own a Shopify Store and generate a comparative research report on competitor products and use that to drive sales for my own store... It's a way to turn content into commerce, or even just to do product research for yourself. RE: Your video speaks to "how hard ... ... checkout flow? Roast away! Comment from Alok Jain(@alokjain): @steveb   The target for this product are qualitative researchers. With the use of AI in this space, data privacy is top of the mind for people. The competitors

... really impressed me. Simple to use, the delightful format of briefing people, only a positive experience. The only downside is that I cannot see the whole call transcript, which is crucial for me. Are there any free/cheap AI alternatives that makes summaries of answers and saves transcripts at the same time? Comment from steve beyatte(@steveb): Oh wait- @Voicepanel just launched today. Maybe they can chime in as this seems like a perfect fit? @asametrical @johnprovine @john_provine1 Comment from ... ... being free is certainly a plus but I'm with you, it's weird that they don't show you the transcript. It also feels like it's way too early to send to real customers (product feels quite sparse