... eating your time now that building isn't the bottleneck? Comment from Camilla S Andersen(@camilla_s_andersen): @pramod_singh16  Really helpful! Especially the point about figuring out patterns. At the beginning, all the data is just too sparse to tell you anything other than that you need more data (= users). Comment from Abdullah Javaid(@abdullah_javaid3): Distribution, and I can put a number on how lopsided it is. I pulled my own numbers this morning. 407 comments earned ...

... 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 ...

... point launching a buggy MVP Comment from Benjamin Baumann(@benjaminbaumann01): Tbh, a small group of users who genuinely love it > a huge waitlist. Comment from (@new_user___26020267b63ea4605738cbb): Launching BillFast today has reinforced the ready enough vs. finished distinction for me: the core flow (create an invoice, preview it, download a client-ready PDF) is narrow and usable, while feedback is now surfacing the edges currencies, taxes, templates, and payment instructions. For a small product, that feels ... ... limited testers. They gave us the first feedbacks for us to make the app better. After we didn't succeeded with a try on LinkedIn, where the feedback was nearly zero, we are on our way to find good alternatives

... 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 ...

... developer option. 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 Rahil Pirani(@rahilpirani): @john__milton  That's the current default. One hop adds just enough related context without pulling in things two or three degrees out. Comment from ...

... 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 ... ... agency or do the cold contacts to journos or writers yourselves? Comment from Fraser(@atomicimagery): @dbul  Love this. In the process of doing this myself. Comment from Johanna John(@johanna_john1): Loved the honestly about newsletter traffic vs

... LinkedIn posts in your voice. It's web-based, and I used Next.js, I used Better-Auth for authentication, and I added 3 strategies: email/password, Google OAuth and LinkedIn OAuth. I used PostgreSQL as the database and Polar (Stripe alternative) for billing. Other tools I used include: Resend for email, Tailwind CSS for styling, and Antigravity IDE. I hope this helps. Let me know if you need more help. Comment from Handuo(@handuo): We're building Copus a curation platform ... ... here , or let me know if you have other times that would work better for you. Comment from Ryan Hendrickson(@ryan_hendrickson): @maria_vannicola  Nice! Was there something that brought you to use Firebase over other alternatives

... 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

... understood in the first 5 10 seconds. Right now the value is strong, but the mental model (job CV scoring ranking) could be made more instantly obvious for first-time users. Curious how you re thinking about onboarding clarity vs feature depth. Comment from Glen(@glen11): @matty_reed  clear positioning is usually the hardest part to get right early on. Curious how you re thinking about scaling it beyond direct landing page traffic? Comment from Glen(@glen11): @mahima_manik ... ... 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

... 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 Alex Robeiko(@alex_robeiko): @steveb  Thanks for those tools options. @Voicepanel is actually the one that covers all what we need. Very timely launch ... ... 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