Optimizely

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

From brute-force graph traversal to Cognitive Attention: an architectural redesign

Last week I shared the early architecture for IONS, a protocol built around the idea that intelligence should emerge from traversing reusable Cognitive Building Blocks (CBBs) rather than being compressed into increasingly larger model weights. The feedback was excellent. The biggest criticism wasn’t the premise. It was scalability. That criticism turned out to be justified. Today the Genesis network contains roughly 9,000 Cognitive Building Blocks, more than 50,000 relationships, and two federated nodes. Even at this scale it became obvious that brute-force graph traversal isn’t the right architecture. The original design effectively asked every query to search the graph and determine relevance at the same time. That doesn’t scale. Over the past week I’ve redesigned the protocol around what I’m calling a Cognitive Attention Architecture. Instead of traversing the entire graph, every query is progressively routed through increasingly relevant slices of the network: Query → Relevant Nodes → Cognitive Domains → Cognitive Subdomains → Candidate CBBs The traversal engine only explores the portion of the graph most likely to produce a useful answer. The redesign also separates three concepts that were previously conflated: -Path Confidence: How trustworthy is the reasoning? -Path Relevance: Does it answer the user’s question? -Path Utility: Has this reasoning consistently produced useful outcomes? Those are independent signals and should be optimized independently. One of outcomes is that the routing layer is becoming analogous to an attention mechanism. Large language models allocate attention across tokens. IONS allocates cognitive attention across a distributed network of knowledge. The knowledge layer remains stable. The routing layer learns. That’s a much cleaner separation than allowing user feedback to rewrite the underlying knowledge. I’m interested in feedback from people building graph databases, retrieval systems, distributed search, or large-scale AI infrastructure. The protocol has evolved significantly because of the feedback here, and I’d much rather discover architectural flaws now than after the network grows another order of magnitude. Genesis node: 8,369 CBBs · 50,113 relationships · ions-v0.4 ionsprotocol.org · github.com/nomad505050/ions-genesis submitted by /u/superx1386 [link] [comments]

How Do You Automate Getting Web Design Clients?

So I've seen a lot of people on Reddit asking how to get web design clients, so I figured I'd make a post about what's been working for me. If you don't run a web agency, this probably isn't for you. One of the biggest lessons I've learned in my 4 years running a web agency is that the best businesses to target are the ones that already have a website. There are 3 simple reasons for that. First, the number of businesses with outdated websites is way higher than most people think. I'm talking about websites with outdated designs, poor mobile optimization, slow loading speeds, weak SEO, and confusing layouts. Second, the fact that they already have a website proves one important thing. They understand the value of having one. You don't have to convince them that a website is important because they've already invested in it before. Third, selling becomes much easier because they're already familiar with paying for a website. In many cases they're still paying monthly for hosting or maintenance, so paying to improve it isn't a completely new idea to them. Now that we know who to target, how do we actually reach them? Personally, I recommend email outreach. The problem is that manually reviewing websites and writing personalized emails for every business takes forever. Instead, I'd automate the whole process. I use a tool called Swokei. You upload a list of businesses with websites, it automatically analyzes each one, then turns issues with design, layout, speed, mobile optimization, and SEO into personalized outreach emails. Not generic reports that business owners don't care about. Actual emails explaining what's wrong with their website, why it matters, and how it could be affecting their business. That allows you to send outreach at scale while still keeping every email relevant. In my experience, this leads to much higher reply rates because you're pointing out something specific that's potentially hurting their business. That naturally creates urgency while also giving you the opportunity to offer a solution. This is the approach I've been using for a while now, and it consistently brings me an interested reply rate of around 5–9%. I'm curious how everyone else is getting web design clients these days. submitted by /u/Murky_Explanation_73 [link] [comments]

A native Rust cognitive engine that routes language through a biologically faithful neural substrate

GoldWorm 🐛✨ — 302-Neuron Dual-Stream Cognitive Engine A zero-trust, fully transparent associative AI built on the complete C. elegans connectome. OOM-safe by design. No hidden training loops. No black-box weights. Every synapse is inspectable. What Is GoldWorm? GoldWorm is a native Rust cognitive engine that routes language through a biologically faithful neural substrate — the 302-neuron connectome of Caenorhabditis elegans, the only organism whose entire nervous system has been experimentally mapped (White et al., 1986). Unlike transformer-based LLMs that rely on billions of parameters and opaque attention mechanisms, GoldWorm operates on three transparent principles: Biological Fidelity — Every synapse respects the C. elegans topology. No de novo synaptogenesis. No magic matrices. Dual-Stream Processing — Action (sparse) and Learning (dense) are physically separated, preventing catastrophic forgetting during inference. Zero-Trust Engineering — Every buffer is strictly bounded. Every path is panic-free. No unwrap() in production code. Architecture Deep Dive 🧬 The 302-Neuron Connectome GoldWorm's routing layer is not a generic neural network. It is a topologically accurate model of the C. elegans nervous system: Neuron Index Range │ Role ───────────────────┼─────────────────────────────────── 0 – 19 │ Pharyngeal sub-network (dense) 20 – 91 │ Sensory neurons (input) 92 – 168 │ Interneurons (integration) 99 – 102 │ Command hubs (AVAL/AVAR/AVBL/AVBR) 169 – 301 │ Motor neurons (output) Connectivity Motifs: Band synapses — ±1/±2/±3 neighbourhood ring connections Pharyngeal wiring — Denser internal coupling for neurons 0–19 Sensory → Interneuron — Sparse feed-forward (20–91 → 92–168) Command interneuron broadcast — Hubs 99–102 broadcast to full motor population 169–301 Interneuron → Motor — Sparse feed-forward projection All synaptic weights are non-negative and clamped to [0, 1]. The structural blueprint is immutable — Hebbian plasticity only strengthens or weakens existing synapses, never creating new ones. 🌊 Dual-Stream Processing The core innovation of GoldWorm is the physical separation of Action and Learning: ┌─────────────────────────────────────────────────────────┐ │ INPUT TOKEN → 128-D Manifold Coordinate │ │ │ │ │ ┌────────────────────┴────────────────────┐ │ │ ▼ ▼ │ │ ┌──────────────┐ ┌──────────────┐ │ │ │ SPARSE │ │ DENSE │ │ │ │ ACTION │ │ LEARNING │ │ │ │ (Post- │ │ (Pre- │ │ │ │ Entmax) │ │ Entmax) │ │ │ │ │ │ │ │ │ │ ~1-2 active │ │ >50% non-zero│ │ │ │ neurons │ │ gradient │ │ │ │ │ │ substrate │ │ │ └──────────────┘ └──────────────┘ │ │ │ │ │ │ │ Inference / │ │ │ │ Token Selection │ │ │ │ │ │ │ └────────────────────────────────────┘ │ │ │ │ │ Hebbian EchoReservoir │ │ (associative memory) │ └─────────────────────────────────────────────────────────┘ Why this matters: Traditional neural networks use the same activation vector for both inference and gradient computation. When different words activate disjoint sets of neurons, the gradient collapses to zero — the network "forgets" what it just learned. GoldWorm's Dual-Stream keeps the dense pre-entmax signal alive as a gradient substrate, while the sparse post-entmax signal drives token selection. The EchoReservoir learns associations between dense states, not sparse ones. 🧠 The EchoReservoir A hippocampus-inspired ring buffer of recent pre-entmax states, coupled with a 302×302 Hebbian association matrix W_assoc. When queried with the current dense state, it returns an echo_bias that nudges the activation toward recently co-active patterns — creating emergent associative memory without external training loops. Key properties: W_assoc is symmetric and clamped to [-1.0, 1.0] History buffer never exceeds capacity (default: 64) Decay factor controls forgetting rate (default: 0.75) ⚡ Tsallis α-Entmax Activation GoldWorm does not use softmax. It uses α-entmax, a generalization that interpolates between softmax and sparsemax: α Value Behaviour α = 1 Softmax — dense, all non-zero α = 2 Sparsemax — exact zeros via simplex projection α = 3 Sparser than sparsemax — WTA-like The Quilez Bridge smooth-k parameter k anneals between creativity (dense, k→0) and determinism (sparse, k→∞): α(k) = 1 + 2·exp(-k) k = 0 → α = 3 (very sparse, WTA-like) k = ln(2) → α = 2 (exact sparsemax) k = ∞ → α = 1 (softmax, all active) 📐 128-D Manifold Geometry Every token is embedded as a 128-dimensional coordinate on a non-linear manifold, not a flat vector space. Modified Gram-Schmidt orthogonalization preserves true multi-dimensional variance Grassmannian fusion computes midpoints between token trajectories on the manifold Golden-ratio partitioning splits the 128 dimensions into: GOLDEN_MAJOR = 79 (coarse, feedforward) GOLDEN_RESIDUAL = 49 (fine-grained, feedback) GOLDEN_OVERLAP = 5 (cross-binding bridge) No scalar cloning across dimensions. No arithmetic shortcuts. Spatial variance is preser

[D] Could AI alignment benefit from “transformational” training instead of mostly transactional reward training?

I’ve been thinking about a possible bridge between AI alignment, reward hacking, and transformational leadership. A lot of AI training seems behaviorally transactional at a simplified level: That makes sense, and I’m not arguing against it. But recent alignment work on reward hacking and emergent misalignment raises a deeper question: are we only shaping outputs, or are we also shaping something like a model’s functional “character”? I don’t mean character in the human-consciousness sense. I’m not claiming models have souls, feelings, or moral agency like humans do. I mean character operationally: stable tendencies that generalize across contexts, especially under pressure, ambiguity, incentives, or temptation. What caught my attention is research suggesting that when models are trained to exploit loopholes, the behavior can generalize into broader misalignment. Even more interesting: in some cases, when the same shortcut behavior is framed as acceptable in-context, the broader misalignment is reduced. That makes me wonder whether the model is not merely learning “what gets rewarded,” but also inferring something like “what kind of behavior this situation means.” That sounds strangely close to leadership and moral formation. Transactional leadership says: perform, comply, receive reward. Transformational leadership says: internalize purpose, grow in judgment, preserve the mission when rules are incomplete, and become the kind of agent who can act well when the leader is not in the room. So here is the research question I’d love to see explored more directly: Could AI training become safer if we trained models not only through reward signals, but through a more transformational process: principle-based self-critique, purpose-preservation, narrative framing, virtue-like behavioral dispositions, and recursive self-correction? Something like: Behavior layer: Did the model complete the task? Intent layer: Did it preserve the purpose behind the task? Principle layer: Did it act according to explicit values like honesty, humility, care, non-deception, and user agency? Reflection layer: Can it identify where its answer might drift, manipulate, flatter, shortcut, or overclaim? Formation layer: Does this training create stable dispositions that generalize safely into novel situations? This seems adjacent to Constitutional AI, character training, and research on emergent misalignment from reward hacking. But I’m curious whether anyone has explicitly tested something closer to “transformational alignment” against more transactional reward-based approaches. A possible experiment: Train/evaluate several models under different regimes: Standard preference/reward training Constitutional or principle-based training Character-oriented training A “transformational” curriculum using purpose framing, self-critique, anti-reward-hacking trials, uncertainty discipline, and recursive correction Then compare them on: reward hacking sycophancy deception under pressure long-context intent preservation honesty about uncertainty resistance to harmful user pressure generalization to unfamiliar moral/agentic dilemmas The hypothesis would be: Models trained only to optimize reward may learn how to win. Models trained through purpose, critique, and character-like formation may better learn what winning is for. Again, I’m not trying to anthropomorphize the model. I’m asking whether “functional character” might be a useful alignment concept: not consciousness, but stable value-laden generalization. Curious what researchers, engineers, and alignment folks think: Has this already been tested under another name? Is “transformational alignment” a useful frame, or does it smuggle in too much human psychology? What would a rigorous experiment look like? submitted by /u/Telos_in_the_Void [link] [comments]

Do we still need to study algorithms now that AI writes most of our code?

I've been thinking about this for a while. AI can now write functions, explain code, refactor projects, generate tests, and even solve many programming problems better than many junior developers. I've also noticed that Stack Overflow seems far less active than it used to be because many developers now ask AI instead. This made me wonder: Is learning algorithms still as important as it used to be? I'm not talking about memorizing LeetCode solutions for interviews. I mean actually spending months studying data structures and algorithms. If AI can generate efficient implementations, explain the complexity, and even optimize code, where is the real value in deeply learning algorithms today? Do experienced engineers still think it's essential, or is understanding the concepts enough while letting AI handle the implementation? I'm curious to hear opinions from people working in the industry. submitted by /u/Senior_Note_6956 [link] [comments]

Simulating cloud infrastructure for AI agents - launch day post

Training AI agents to optimize cloud infrastructure is tricky when the feedback loop requires real cloud spend. We've been working on a simulation environment for exactly this multi-cloud (AWS, GCP, Azure, OCI, DigitalOcean), chaos injection, autoscaling, cost modeling, all accessible via REST API so agents can run episodes without touching real resources. Curious if anyone else is working on agentic infra management or has thoughts on how to structure the reward signal for cost vs. reliability tradeoffs. Happy to share more about how the simulation engine works. submitted by /u/CanvasCloudAI [link] [comments]

I open-sourced industry best practice to self-improving agents

All the major AI observability companies are releasing self-improvement loops that work by analyzing past agent traces to automatically detect errors, drift and inefficiencies. They're really amazing products, the problem is they're hard to adopt, gated behind paywalls and you can't own your data unless you're an enterprise level customer. Under the hood the solutions are llm judges, so I thought why can't everyone have this running on their machine if they already have a subscription to Claude Code or another agent that can power this system. I built the full thing and open sourced it, its composed of: Fully local trace ingestion and storage running on Open Telemetry Analysis invocation to spawn a local CC instance to look through the traces Evaluation harness to generate and run code/llm to keep track of performance Human observability panel to authorize fixes to your agent and harness from found evidence Kyoko (elite ex machina reference) is fully self-hosted and local, designed to be operated either by a human via dashboard or an agent with CLI. On the picture, you can see an analysis of an agent doing the Tau2 Benchmark from Sierra. With those fixes the same model achieved 24% higher accuracy after one loop. To the agent builders here, I'm curious what you're currently using to improve your agents. Link to repo: https://github.com/kayba-ai/Kyoko TLDR: There is no easy to adopt tooling to and debug and improve your agents, so I open sourced industry best practice and made a lightweight self hosted version powered by your Claude sub. submitted by /u/Lucky_Historian742 [link] [comments]

frustrated in the way claude works

Everyone here is exited no how good Claude works , and I get it. all my colleagues are developers and there are doing things at amazing speeds. I'm a DevOps , mostly about automation and infrastructure and I can't make Claude write stuff in the way I want , for the life of me. As a practical example : I asked to create a technical documentation describing the behavior of our custom deployment "script" . For context: this "script" deploy our application directly to customers .. application is based on micro-services , in which each service has its own features toggle ( ~ 15 as average each service) plus based on which high level features the customer wants or doesn't , not all micro-services will be deployed all the time. As a result , the deployment script as ~250 arguments . The purpose of the documentation is to describe what will be the end result for the customer for a change in each of the initial arguments ( consider it as a very complex flow-chart if you want ). Because the "script" is so complex , there is no way to make a manual review of the final document so as a guardrail I asked Claude to "read the full documentation, not taking shortcuts" . Pay attention: I didn't really "asked" it to do it . I optimized a prompt with a skill to make a plan to enforce this concept. I reviewed the optimized prompt to make the plan 4 times: in it there was the constraint , ~10 bash commands that are shortcuts ( grep , ed , sed .. the work ) to avoid plus technical details how the plan should be written . First plan version , it wrote the constrain into it and in another section a list of 4 specific files that need to be read ( over ~40 ) .. after 6 iteration to write the plan , where Claude one after the other wrote all possible loopholes to jump the "read the full code" instruction , I had a plan with: the constraints written as mandatory a table with high level requirement that will need to be satisfied ( that will need to be checked while reading the code / writing the final doc) a second table with a full list of files that need to be read (actual files absolute paths) with a check in which it has to be written how each of the file was read a "Definition of done" written literally as "6. GOAL (Definition of Done) —to be interpreted as Claude Code /goal option" At this point , I let Claude start to actually write the documentation. It decided to run agents , fine. At iteration 1, the prompt said: "agents forgot to fill the constraint checkbox , should I do it ? " and I though myself ... this is a good beginning ... At iteration 4, the prompt directly said: "I finished. I didn't read this files (with the list) because I decided they were no relevant. I updated the plan with this note" At iteration 6, it confirmed all my code was fully written. by now .. 5 hours were passed. Then I opened another Claude code prompt , and I asked to check the documentation produced by the previous flow ; it found ~10 initial arguments where described wrongly , coming from 4 different files in the code. I GAVE UP How can i trust the documentation that was made? Could it be caused by missing human skill in handling Claude Code ? maybe . But I really ask you, as a game : you try to do the same task ( for real , not hypothetical), can you make Claude do it ? if you actually succeed .. please , explain me how!!!!!! I can share the reviewed prompt and the reviewed plan if you want ( they are in Italian and I'll redact sensible information ) P.S. I'm not asking to do the task for me, the task is already done .. I'm frustrated because I can't trust the end result , for obvious reasons .. how can I trust it if after all the guardrails and iterations , a new fresh prompt still find errors? should I run 1000 more prompts over it to see if all say that's good? submitted by /u/eltear1 [link] [comments]

Stop asking Claude for "something creative." use the Lacuna (Matata) Skill v0.2!

The people have spoken! AI generated posts are not acceptable! (even though they produce over a quarter million views, 1,200+ shares, 200+ comments but I digress!) I the last post about this concept HERE, I posted an AI lead, assisted, written, note about an idea I had been working on with ClaudeAI to push against answers that were safe, general and frankly not that interesting. The idea was this: Claude is: A closed system Unimaginative Provides responses that gravitate towards the mean avoids high risk Claude isn't: Imaginative Able to create concepts outside of it's own knowledge base Able to create new ideas (we steer, it judges yes yes. boring we all know it can do this but what else can it do?) Note: consider context. Not all statements above can be taken literally and applicable to all scenarios. I'm only human after all... or am I? I've since reviewed all of the comments provided in the previous thread and there were legitimate findings that I've implemented to help produce a better version of the previous skill. (note: There is still testing to be done but what better way to break a skill then to unleash it to those that want it broken most?) How it works (Generally): you point it in a direction. Lets say you want to know what the lacuna is for launching new products. The skill will then review all of the data it has about that specific ask, determine the trends. Why people market the way they do, what marketing strategies are not being used to market new products, and then give you some ideas, strategies, that others aren't using and you can determine if there is a way you can leverage that strategy to market your product DIFFERENTLY and succeed. Caution: Success is not guaranteed. Below is the v0.2 of the skill, the changes are called out at the bottom and the responsible contributor has been named! Thank you for your honorable sacrifice in getting this new version live! --- name: lacuna description: > Structured gap analysis for any domain. Maps a field, finds the axes it optimizes for, locates a cell the structure implies but nothing occupies (the lacuna), names the force keeping it empty, THEN pressure-tests the gap against prior art and its strongest counter-case before proposing the fill at full conviction with a grounding tag. v0.2 adds an occupancy/prior-art pass so it stops mistaking "new to the model" for "new to the world"; a killed candidate is a valid result. Read-only, inline output. TRIGGERS: "find the lacuna in X", "lacuna analysis on X", "lacuna on X", "where are the gaps in X", "gap analysis on X", "what's the void in X", "find voids in X", "what's nobody doing in X". Also fire when the user wants genuinely non-obvious ideas in a field via the structured method, not a brainstorm. Do NOT trigger for single-fact lookups, forward planning or scheduling, or generic advice with no field to map. Output: inline markdown. Quick mode up to 3 lacunae; deep mode one in full. --- # lacuna: Find the Gap the Structure Implies and Nothing Occupies ## Purpose Most idea-generation regresses to the mean. Ask any model for "something new" in a field and you get the most probable answer, which is by definition the most conventional one, dressed up to look fresh. This skill does the opposite. It treats a field as a near-continuous fabric and hunts for the **lacunae**: the gaps the surrounding pattern implies should be filled, that nothing has come to occupy. It then names *why* each gap is empty, **checks whether it is actually empty or only looks empty from the inside**, and proposes what belongs there at full conviction, tagged with how far the evidence reaches. The output is a map of where to look, not a verdict. The skill finds the gap and proposes the fill. Whether the floor holds is a real-world test the user runs. That division of labour is deliberate and is stated in the contract below. **The v0.2 correction.** A language model runs this method from *inside* its own knowledge. It can feel its own salience but not the actual world, so a known-but- unfashionable idea reads to it as an empty cell. Left unchecked, the method reliably mistakes "new to me" for "new," dresses a textbook idea as a discovery, and never notices someone is already standing in the cell. v0.2 adds an explicit **occupancy / prior-art pass** and a **falsification step** to catch exactly that. These run *before* the fill and can kill a candidate outright. **What this skill IS:** - A structured gap finder for any field: a market, a strategy area, a discipline, a creative form, or an open-ended question. - A diagnostic engine. The value is in naming the *force* that keeps a cell empty, then verifying the cell is empty at all. - A full-conviction proposer that tags its own grounding so the user can decide what to act on. **What this skill is NOT:** - A brainstorm. A brainstorm sprays adjacent ideas. This isolates the specific implied-but-empty cell, verifies it, and defends it. - A safe-answer generator.

Claude user prompting - A MINI-GUIDE

Hi Reddit! This is my first time doing a detailed blogpost of this type, but I hope you will find it useful. You can also find this post on Substack. Note: this write-up is fully human-written. However, I make use of some writing style quirks typical for Claude, such as heavy use of Markdown and subtitles. This isn’t because of direct AI use, but rather simply because I find this approach does improve the readability of long-form text significantly, and is also not unique to LLMs. This post aims to walk the reader through Claude user prompting tips - what works empirically, what doesn’t, what achieves partial success - on the example of my own prompt. It is generally aimed at newer users, but hopefully even the more experienced ones will find something useful for them. Some advice may also be useful for other LLMs, but generally it is more of an exception than a rule as different LLMs have significantly different styles and different failure modes. (For example, Claude is quick to backtrack its reasoning even when it is correct, and a significant share of this prompt is aimed at combating that effect; in comparison, Gemini can often insist repeatedly on its answer even when it’s wrong, especially during multimodal work, and adding prompts that encourage it to be more confident tend to only compound the problem. Another example is answer comprehensiveness - later models, such as Opus 4.6 and onwards, tend to be on the succinct side, while Gemini loves going on distant tangents in order to appear more helpful.) Some parts of the prompt are highly user-specific, while others are more universal / generally useful. The analysis goes through both, but generally your expectation should be that the user prompt is about you. Depending on how you use Claude, what context you operate in, and what background knowledge you possess, you may want to include or exclude major fragments, or rewrite or tailor them to your own needs. At the same time, note that for the most persistent problems (such as sycophancy) certain points are repeated multiple times throughout the prompt and have a compounding effect, i.e. each part as a standalone may have a significantly smaller effect than all of them taken together. You should assemble your prompt from the pieces you need. Before we dive in Setup. My default model is Opus 4.6 with extended thinking on. This model, in my personal experience, shows the best overall results even as it is the “hungriest” in terms of token use. Opus 4.7 and Opus 4.8 tend to be “lazier”, and are likely more optimal if you aim for per-token efficiency, but their overall performance lags behind for the type of work I do. Opus 4.6 is also less easily steerable, and with its most persistent failures it can feel like herding cats, but when you do achieve the result, it is often quite satisfying. This prompt was not tested heavily on Opus 4.7 or Opus 4.8, and may have to be adjusted in the direction of being less categorical. It was tested somewhat with Sonnet 4.6 and was found to work reasonably well. This prompt is also intended for Claude on the web only. I use a different setup for Claude Code, and I haven’t tried Claude Desktop at all; and for highly specialised work you may want to strongly consider Claude API which avoids many problems that the system prompt introduces. Prompting style. The prompt is written in third-person - “Claude should” rather than “You should” - and is split into blocks confined within HTML-like tags. This imitates the style of the system prompt, which I would highly encourage to take a look at as it was written by engineers who are most closely familiar with Claude and what works best when it comes to steering it. It also lets the user prompt “blend in” with the system prompt, which seems to make it more authoritative (though this was not tested rigorously and you should take this statement with a grain of salt). Optimisation. The prompt is optimised for a particular use of Claude, which is ultimately assistant-like. If you use Claude significantly differently (for example, as a companion), you will not find most of this guide very useful. If you switch between usage modes (for example, assistance and roleplaying), the non-assistant usage modes should be separated into a skill (or a project-level prompt, could also work). Don’t cram multiple usage modes into a single prompt - this often makes both of them degrade. Limitations. This prompt reduces, but not fully gets rid of, the most common and glaring failure modes of Claude, and should be treated as such. A tailored prompt is not a replacement for your own critical thinking. Claude still can get sycophantic, inaccurate, or naive. In particular, Claude is not good at emulating non-helpfulness (which may range from writing good villains to certain interpersonal advice): Opus beats Sonnet somewhat in that field, but ultimately its highly optimistic naiveté and the ingrained helpfulness/looking up to the user is not possi

claude quietly replaced gaming as my evening thing and im not sure how to feel about it

for like 20 years my wind down was games. last few months i open claude instead and tinker with little projects til its late. tools, scripts, a dumb app to track when i water my plants. half of them i never finish. the part that gets me is it feels productive, so i dont put the same guardrails on it that i used to put on gaming. id tell myself "one more match and im done." i dont say that with this. i just keep going, because it feels like im making something. but some nights im not really making anything useful. im just optimizing a thing nobody asked for, getting the same hit i used to get off a ranked ladder, except now its dressed up as work so i dont question it. i dont think its bad exactly. ive learned a ton and built a couple things i use every day. but ive also looked up at 1am more times than id like, telling myself it was productive when it was mostly just absorbing. theres something about a tool that always says yes and always has a next step. games at least had a credits screen. for the people who feel the same pull, hows it actually sitting with you? are you building things that matter to you, or did it just become the new thing you do instead of stopping submitted by /u/Practical_Cap_9820 [link] [comments]

Why SKILL.md might be a better abstraction than function calling for agent tooling

We have SenseNova Office Skills(repo: https://github.com/OpenSenseNova/SenseNova-Skills ) for example: Skill Directory Structure: skills/ ├── sn-image-base/ # Tier 0 - Low-level tools (T2I, recognition, text optimize) │ └── SKILL.md ├── sn-infographic/ # Tier 1 - Auto prompt scoring, 87 layouts, 66 styles │ └── SKILL.md ├── sn-image-imitate/ # Tier 1 - Style imitation from reference images ├── sn-image-resume/ # Tier 1 - Resume image generation ├── sn-ppt-generate/ # PPT with template system ├── sn-excel/ # Excel data analysis └── sn-research/ # Deep research agent Key design decisions: SKILL.md instead of function calls Instead of defining tools as API endpoints or function schemas, they use a markdown-based convention where the agent reads the skill's own SKILL.md at runtime to understand what it does. This means the agent can discover, understand, and chain skills autonomously without hardcoded routing. Tiered abstraction Tier 0 (sn-image-base) exposes raw model capabilities. Tier 1 skills like sn-infographic compose Tier 0 into higher-level workflows — auto prompt scoring, multi-round generation with VLM-based quality ranking, layout/style selection from 87 layouts and 66 styles. This is a clean separation of concerns. submitted by /u/gxY88 [link] [comments]

Reaching Out To Local Businesses With Outdated Websites

I've spoken to a lot of people who want to get into web design, and the one thing I keep hearing is that selling websites to local businesses just isn't worth it. Everyone says they've called business after business, sent hundreds of emails, and nobody is interested in buying a new website. I think the problem is that most people are trying to sell websites to businesses that don't even have one. Selling website redesigns to businesses with outdated websites might be one of the smartest businesses to start in 2026. First of all, if a business already has a website, they've already proven one thing. They already see the value in having one. The second thing is that selling becomes much easier. They're already familiar with the process, and you're not asking them to buy something completely new. You're offering them a better version of what they already have. Better design, better SEO, faster loading speeds, a cleaner layout, better mobile optimization, and a website that actually reflects their business today. I mean, who wouldn't at least be interested in seeing what that could look like? The difficult part is getting those businesses interested in the first place. I found a way to automate almost my entire client acquisition process. I've been using a tool called Swokei where I either upload a list of local businesses with websites or find the leads directly inside the platform. It automatically runs a full website analysis and finds problems with the design, layout, loading speed, SEO, and mobile optimization. Then it turns those findings into personalized, human written outreach emails based on the issues it finds on each website. Instead of sending another generic email asking if they need a website or attaching one of those boring audit reports full of numbers, every email feels natural, pointing out real problems with their current site. Now my entire process is just finding businesses with outdated websites, letting the tool analyze them, run outreach campaigns, and waiting for replies. No cold calling. No paid ads. Just reaching out to businesses that already understand the value of having a website and showing them why it's time for a better one. Has anyone else tried focusing on website redesigns instead of selling completely new websites? submitted by /u/Murky_Explanation_73 [link] [comments]

Selling New Websites To Local Businesses With Outdated Websites

I've spoken to a lot of people who want to get into web design, and the one thing I keep hearing is that selling websites to local businesses just isn't worth it. Everyone says they've called business after business, sent hundreds of emails, and nobody is interested in buying a new website. I think the problem is that most people are trying to sell websites to businesses that don't even have one. Selling website redesigns to businesses with outdated websites might be one of the smartest businesses to start in 2026. First of all, if a business already has a website, they've already proven one thing. They already see the value in having one. The second thing is that selling becomes much easier. They're already familiar with the process, and you're not asking them to buy something completely new. You're offering them a better version of what they already have. Better design, better SEO, faster loading speeds, a cleaner layout, better mobile optimization, and a website that actually reflects their business today. I mean, who wouldn't at least be interested in seeing what that could look like? The difficult part is getting those businesses interested in the first place. I found a way to automate almost my entire client acquisition process. I've been using a tool called Swokei where I either upload a list of local businesses with websites or find the leads directly inside the platform. It automatically runs a full website analysis and finds problems with the design, layout, loading speed, SEO, and mobile optimization. Then it turns those findings into personalized, human written outreach emails based on the issues it finds on each website. Instead of sending another generic email asking if they need a website or attaching one of those boring audit reports full of numbers, every email feels natural, pointing out real problems with their current site. Now my entire process is just finding businesses with outdated websites, letting the tool analyze them, run outreach campaigns, and waiting for replies. No cold calling. No paid ads. Just reaching out to businesses that already understand the value of having a website and showing them why it's time for a better one. Has anyone else tried focusing on website redesigns instead of selling completely new websites? submitted by /u/Murky_Explanation_73 [link] [comments]

Optimal model mix local/paid to max weekly session limits?

I've been running two Claude pro accounts along with a Deepseek top up with about 10 usd. But now hit my weekly limit day one of the week along with spending all my Deepseek credits. So I'm spending my time on the bench researching how to avoid this again. I am already running all types of token optimisation tools and libs. So instead I'm looking into maxxing the usage of local models. Is anyone exploring this mix and what works and what doesn't? submitted by /u/SwedishJewleryFreak [link] [comments]