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Key takeaways Summarizing conversation history can reduce costs by up to 60%. Implementing an effective summarization algorithm is key to efficiency. Balancing detail and brevity in summaries is crucial for context. Optimized context windows lead to faster response times and lower latency. The problem Startups leveraging large language models (LLMs) often face significant costs associated with managing context windows during conversations. Each token processed incurs a cost, and as conversations grow, replaying entire histories can lead to runaway expenses. Founders and engineers encounter this issue particularly during customer support interactions or chatbots, where lengthy dialogues require constant context retention, drastically inflating operational costs. What we found Our research indicates that instead of replaying the entire conversation history, summarizing the dialogue can maintain context while drastically reducing token usage. By distilling key points and intents into a concise summary, we can effectively minimize the number of tokens processed, leading to major cost savings without sacrificing the quality of interaction. This non-obvious insight repositions how we approach conversation management in LLMs. How to implement it Start by selecting a summarization algorithm suitable for your use case. Techniques like extractive summarization (e.g., using TextRank) can identify and retain essential sentences from conversations, while abstractive methods (e.g., fine-tuning a transformer model) rephrase the content. Next, integrate this summarization step into your workflow: after each interaction, generate a summary that captures the main points. Ensure that the summary is stored and utilized as context for subsequent interactions, replacing the need for the entire conversation history. Monitor token usage before and after implementation to quantify cost savings. How this makes life easier By summarizing conversation history, startups can see a reduction in c

Your docs pipeline, from the terminal PaperQuire v0.2.0 ships with a full command-line interface. Everything you can do in the desktop app — render, template, brand — now works from your terminal. This means you can automate document generation, integrate PaperQuire into CI/CD pipelines, and batch-process entire directories of Markdown without opening the GUI. Quick start If you installed via Homebrew, the paperquire command is already on your PATH: brew install --cask paperquire/paperquire/paperquire Verify with: paperquire version The simplest render is just: paperquire report.md This produces report.pdf in the same directory, using the default template. What you can do Render with options Apply templates, set metadata, control output: paperquire report.md -t corporate --title "Q2 Report" --author "Jane Doe" paperquire spec.md --page-size a4 --margins 0.75,0.75,0.75,0.75 --no-toc paperquire draft.md --watermark "DRAFT" -o drafts/draft.pdf All 22 flags from the desktop app's Document Setup panel are available. See the full list in the CLI Reference . Pipe through stdin/stdout PaperQuire works in Unix pipelines: cat notes.md | paperquire - --stdout > out.pdf Use - as the input file to read from stdin, and --stdout to write the PDF to stdout instead of a file. Batch render directories Render every Markdown file in a directory with one command: paperquire batch ./docs -o ./out paperquire batch ./project --glob "**/*.md" -t corporate paperquire batch ./docs --concurrency 8 --continue-on-error Use --dry-run to preview which files would be rendered without actually producing output. Manage templates List, inspect, create, and share templates: paperquire templates list paperquire templates show minimal-clean paperquire templates init my-template paperquire templates export corporate -o corporate.pqt paperquire templates import ./shared-template.pqt We also launched a community templates repo where you can browse all built-in templates and submit your own via pull request.

I use my SSH manager every day. I also use a separate monitoring tool every day. For a long time I just accepted that these were two different things. Then one day I was SSH'd into a server that was behaving weird. I wanted to check if it was CPU or memory, but I had to open a different app, find the server in there, and wait for the dashboard to load. It took maybe 15 seconds. Not a huge deal. But it broke my flow every single time. I already had an SSH connection open to that server. Why was I opening a second thing just to see what was happening to it? That's what pushed me to build server monitoring directly into Termique, the SSH manager I've been working on. The interesting part: reusing the existing SSH connection SSH connections aren't just for terminals. The protocol supports multiple channels over a single TCP connection. You can have a terminal session running in one channel while sending short exec commands through another channel on the same connection. That's how the monitoring feature works. When you open the metrics panel for a server, Termique creates a separate exec channel on the existing SSH connection and polls /proc/stat for CPU, /proc/meminfo for RAM, and /proc/loadavg for system load. Short-lived commands, called on an interval, over the connection you already have open. No second SSH handshake. No separate auth. Just another channel on the same pipe. The tradeoff: you do need an agent I want to be upfront about this. The monitoring feature requires a small agent installed on each server. It's not agentless. I considered going agentless, relying entirely on /proc reads through exec channels. That works fine on most Linux servers. But the agent makes it easier to handle edge cases properly and opens the door for future features like alerts and longer history retention. Without it, I'd be fighting a lot of fragile shell parsing. If you're managing Linux servers, it's a one-command install. Non-Linux systems aren't supported yet. That's a real l

What building cross-service RBAC taught me about the difference between a fast check and a correct one VaultPay is a wallet microservice I built on top of AuthShield. Previous parts: Part 1 is here: I Built AuthShield and Immediately Knew It Wasn't Enough Part 2 is here: The Silent Failure I Never Saw Coming: What VaultPay Taught Me About Consistency Under Failure Part 3 is here: I Started With a Blocklist. That Was the Wrong Instinct and VaultPay Taught Me Why. Part 4 is here: I Watched Money Move Twice From the Same Request. That's When I Understood Idempotency. Part 5 is here: I Almost Hashed a Document Number That Needed to Be Read Again When I designed JWT validation for VaultPay, the only thing I was optimising for was speed. Local verification, no network call, decode the token with the shared secret, read the claims, move on. Every request gets this. It's fast - no round trip to AuthShield, no added latency on the hot path. That felt like the obvious right answer for a system processing financial transactions, where every millisecond on the request path matters. Then I asked myself a question I hadn't thought through properly: what happens if an admin gets deactivated in AuthShield right now, this second, while they still have a valid token sitting in their browser? The answer, with pure local validation, is uncomfortable. Nothing happens. The token is still cryptographically valid. The signature checks out. The claims say role: admin . VaultPay has no way of knowing that AuthShield revoked this person's access thirty seconds ago, because VaultPay never asked AuthShield. It just trusted the token. That's the moment dual-mode validation stopped being a performance optimisation and became a correctness requirement. Two Services, No Shared Database VaultPay and AuthShield are separate microservices with separate databases. AuthShield owns user accounts, login, JWT issuance, and role management. VaultPay owns wallets, transactions, KYC, and admin operations on t

I log commutes in a spreadsheet because mobility apps smooth over the ugly legs. Last week I added a column I should have tracked years ago: carry seconds ? time from curb to platform when stairs replace ramps. The hidden leg My one-wheel leg is fine on paper. Three metro exits on my route have no elevator during maintenance. Carrying a 14 kg wheel down 22 stairs does not show up in trip duration. It shows up in whether I arrive annoyed enough to skip coffee. What I logged (one week) Exit Stairs Carry time (s) Mood after (1-5) North gate 22 38 2 Side ramp (control) 0 8 4 East stairs 16 29 3 Battery delta on those days? Within noise. Mood delta? Not noise. A cheap decision rule I turned this into a go/no-go check before leaving: if stairs > 15 AND carry_weight_kg > 12: prefer transit-only or locker elif stairs > 0 AND wet_floor: walk the wheel (no riding in station) else: ride It is blunt. It works better than pretending every leg is rideable. Assumptions up front Wheel weight includes pads and charger pouch (~14 kg for my commuter setup). I am not timing competitive carries ? just whether I can do this daily without hating it. Your threshold differs if every exit has elevators. What I would do differently I would log carry seconds from day one, same tab as distance and battery percent. Range math without carry math is incomplete for anyone who mixes metro and one-wheel. I work around personal EVs and sometimes cross-check specs on the official Kingsong catalog. https://www.kingsong.com/collections/electric-unicycle

When the enemy is too strong to attack directly, attack what they hold dear. They will come to you...

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There is a habit that wastes more time than anything else when using Claude. Save this :) Writing the same instructions over and over again. Every session, you re-explain your role. You re-describe your writing style. You re-state your formatting preferences. You re-paste your company context. You re-specify what you want the output to look like. Then you do it again tomorrow. And the day after that. And the day after that. Over a month, you waste hours on instructions you have already written. Not new thinking. Not new requests. Just the same setup, repeated endlessly. Claude Projects and Skills fix this completely. Projects let you save context once and have it applied to every conversation automatically. Skills let you save entire workflows as reusable commands that you can trigger with a single sentence. Together, they turn Claude from "a tool you use from scratch every time" into "a system that already knows everything and just needs your specific request." Here is how to set them up from zero. What Are Claude Projects A Claude Project is a container for conversations that share the same context. When you create a Project, you upload knowledge files and write a system prompt. Every conversation inside that Project automatically has access to those files and follows those instructions. No re-explaining. No re-pasting. No re-describing. The context is always there. Example: you create a Project called "Content Marketing." You upload your brand guidelines, your editorial calendar, your top-performing articles, and your audience personas. You write a system prompt: "You are my content strategist. You know our brand voice, our audience, and our content strategy. Every piece of content should match our guidelines and target our defined personas." Now every conversation in that Project - brainstorming headlines, drafting articles, analyzing competitors - starts with full context. Claude already knows your voice, your audience, and your standards. One setup. Unlimited

Two things happened this month and they tell you everything about where AI is actually going. Coinbase quietly cut its AI bill nearly in half. Open models, smarter routing, better caching. No drama. A finance footnote that happens to be a glimpse of the future. And Dario Amodei published another essay. Not a tweet. An essay. The kind of sprawling, twenty-thousand-word civilizational scripture he keeps handing down from the mount. This one is called "Policy on the AI Exponential," and the gist is that AI is about to hand humanity "almost unimaginable power," that our institutions are too immature to hold it, and that therefore the government should be able to test, gate, and block frontier models before mere mortals get hurt. One of these is a price cut. The other is a prophecy. I want to talk about the prophecy. The robes Let me be fair before I am not. Dario is not a dumb man and he is not a fraud. He runs one of the best labs in the world. The safety concerns are not all imaginary. Misuse is real. I am not the guy arguing that anyone should be able to download a bioweapon recipe for a laugh. If that is the bar, sure, regulate it. Nobody serious disagrees. But watch the move he keeps making. Every few months the prophet descends with a new text. The stakes are always civilizational. The language is always biblical. "Unimaginable power." A "decent possibility" of "significant enduring job loss." Disruption that will be "unusually painful." Humanity handed a force it is not mature enough to wield. He is not describing a product roadmap. He is describing a flood. And conveniently, he is also selling the ark. That is the part that should make you tilt your head. Read the actual proposal Strip the poetry off "Policy on the AI Exponential" and here is the machinery underneath. Mandatory third-party testing for any model above a compute threshold. Authorized evaluators. Security standards. Incident reporting. Government authority to block or reverse a deployment that fail

Your CI catches the npm vulnerability. Your developer is already three branches away and one standup behind. The package is installed, the lockfile regenerated, the import wired into a service, and the human who made that decision did it on a Tuesday afternoon with a tab open to Stack Overflow. Now the scanner is yelling. From the terminal, that is not security. That is grief counseling. That is the frame Sonu Kapoor lays out in a DevOps.com essay this week, and the engineering bones of it are correct. A scanner is not a gate. It is a status check. Kapoor's argument is about feedback loops. A developer installs, codes, commits, pushes. Only then does CI run. By the time the finding surfaces, the decision to add the package, and the context for why, has evaporated. So has the lockfile churn that caused it. What started as "is this package safe?" becomes "fix this in a different sprint." The scanner did its job. The fix is now a project. He backs it with a small case study from the NestJS repo: a scan of package-lock.json returned 1,626 resolved packages and 25 vulnerabilities. Of those, 12 were directly fixable. Thirteen were transitive, buried in upstream graphs, waiting on someone else's release. In a pipeline-first workflow, every dependency hop is a separate commit and a separate run. (Multiply by the number of services your team owns. Then by your runner-minutes budget. Send me the bill.) The arithmetic gets ugly quickly. A single lockfile with more than fifteen hundred resolved packages is not exotic for a working Node app, it is the default. The chance that the first time anyone looks at that graph is during a pipeline run, after the merge intent is already in the reviewer's queue, is the structural bug. Where the essay is right, and where it gets too tidy Concede the obvious. CI is not the problem. CI is fine. It runs uniformly, it cannot be skipped, and it is the right place to fail a build when an OSV record drops mid-week against a dependency that was clea

In the world of wearable health technology, the holy grail has always been moving intelligence from the cloud to the edge. Waiting for a cloud server to analyze your heart rhythm is not just a latency issue—it's a privacy and battery life concern. Today, we are diving deep into TinyML , Edge AI , and ECG signal processing to build a real-time abnormality detector. By leveraging TensorFlow Lite for Microcontrollers and the versatile ESP32 , we can process raw electrocardiogram (ECG) data locally. This approach ensures low-latency detection of arrhythmias while keeping sensitive medical data on-device. If you've been looking to bridge the gap between high-level deep learning and low-level embedded systems, you're in the right place! The Architecture: From Raw Signal to Insight 🏗️ The pipeline involves capturing a high-frequency analog signal, cleaning it, and feeding it into a quantized Convolutional Neural Network (CNN). Here is how the data flows through our ESP32: graph TD A[Raw ECG Signal/Sensor] -->|ADC Sampling| B(Preprocessing: Bandpass Filter) B --> C{Buffer Management} C -->|Windowed Segment| D[TFLite Micro Inference Engine] D --> E{CNN Model Classification} E -->|Normal| F[Log: Sinus Rhythm] E -->|Abnormal| G[Trigger Alert: Arrhythmia] G -->|Bluetooth/Wi-Fi| H[Mobile Dashboard] Prerequisites 🛠️ To follow this advanced guide, you'll need: Hardware : ESP32 (DevKit V1 or similar). Sensor : AD8232 ECG Module (or simulated ECG data). Software : Arduino IDE or PlatformIO. Frameworks : TensorFlow Lite for Microcontrollers (TFLM), EloquentTinyML (optional wrapper), or the standard C++ TFLM library. Step 1: Model Training & Quantization 🧠 Before we touch the C++ code, we need a model. Typically, we use the MIT-BIH Arrhythmia Database to train a 1D-CNN. The crucial step is Post-Training Quantization . Since the ESP32 doesn't have a dedicated NPU, we convert our 32-bit float model into an 8-bit integer (INT8) model. This reduces the size by 4x and speeds up inference s

We've all been here. A new form shows up, you install React Hook Form, add Zod or Yup, and in ten minutes you have something that "works." The problem doesn't surface that day. It surfaces three months later, when the same VIN you validate in the create car form also has to be validated in edit , in import from Excel , and it turns out the rule —"17 characters, the last 5 numeric"— is written three times, each one slightly different, and none of them lives in a place you can point to and say "here is what a valid VIN is." A typical form with a library looks roughly like this: const schema = z . object ({ vin : z . string (). length ( 17 , " The VIN must be 17 characters " ), miles : z . number (). min ( 0 , " Miles cannot be negative " ), // ...and 8 more fields }); const { register , handleSubmit , watch , formState : { errors }, } = useForm ({ resolver : zodResolver ( schema ), }); It works. But if you stop to look at it, you're paying three costs that almost never get named: 1. Clean code dissolves. The business rule ends up scattered across the schema , the resolver , the register calls, the Controller s, and the JSX. The knowledge — what makes a car valid — has no home. It's wired into the UI. And what's wired into the UI doesn't get reused: it gets copied. 2. Performance and coupling are paid silently. These libraries live on subscriptions: watch , re-renders on every keystroke, internal state to keep in sync. For a contact form, who cares. For a screen with 15 fields, sub-forms, and cross-field validation, your component is tied to the library's lifecycle —not yours— and you start fighting it instead of using it. 3. Developer convenience is a trap. It's wonderfully convenient at first . But that same rule: how do you test it without mounting a component? How do you move it to the backend? How do you translate it into two languages without polluting the schema? Everything the library gave you for free, it charges you for the day you need to step outside its mo

So this weekend I spent $200 solving a $2 problem. Not because I was careless. Not because the system was broken in the old way. It happened because the tool was powerful, fast, confident, and wrong for just long enough. That is the strange thing about AI systems. They do not always fail loudly. A cloud server goes down, an alert fires, a dashboard turns red, someone opens an incident bridge, and the team knows what kind of movie they are in. AI failure is softer. The answer looks useful. The workflow keeps moving. The agent tries another path. The model explains itself beautifully. The bill keeps climbing. With cloud reliability, we learned how to survive machines failing. We built retries, failover, backups, autoscaling, health checks, runbooks, and incident reviews. The cloud taught us that infrastructure is never perfect, so systems must be designed to bend without breaking. AI is teaching us something different. The machine may be running perfectly and still produce the wrong result. The API may be healthy, the latency may be fine, the token stream may complete, and the business outcome may still be bad. That is why AI Site Reliability is going to become its own serious discipline. It will not be enough to ask, “Is the model available?” We will have to ask, “Is the model still useful?” “Is it drifting?” “Is it spending too much?” “Is it using the right tools?” “Is it looping?” “Is it making the same mistake with more confidence?” “Is a human needed before this continues?” In the cloud world, uptime was the king metric. In the AI world, usefulness will matter just as much. A model that is always available but often wrong is not reliable. An agent that finishes every task but spends 100 times more than needed is not reliable. A chatbot that gives answers with perfect grammar but poor judgment is not reliable. The next generation of reliability engineering will care about cost, correctness, context, and control. Cost matters because AI turns thinking into metered

My kid said he wanted to do origami. I sent my Agent a single sentence asking it to handle the origami materials. It searched for tutorials on its own, printed out the origami guide along with sheets of paper. Later I wanted a different color, so I told it again, and it reprinted everything. From "I want to do origami" to having the materials in hand — under five minutes. In that moment, I felt that what changed might be more than just efficiency. The "Middle Steps" That Got Eliminated The origami isn't an isolated case. Lately I've noticed that the thing my Agent eliminates the most in my life is "middle steps." Market stall supplies. Before, when I received supply lists for my stall, things were scattered. Now the whole flow is connected: information comes in, gets automatically added to my shopping list assistant (another Agent of mine), and the items are already waiting in the shopping cart. Two scenarios, same pattern: before, you do N steps to get the result. Now you do 1 step — say "I want this." I call those eliminated N-1 steps middle management cost. What Is Middle Management Cost? It's not money. It's your attention and the number of decisions you make. Every time you open a search box, filter results, compare options, make a decision — your attention gets drained. Individually it's not much, but accumulated over a day, you feel "tired but haven't really done anything." That's the hidden damage of middle management cost. The logic of traditional tools is: I give you better tools, you do the work yourself. Better search engines, faster printers, more convenient shopping apps. But no matter how good the tools are, you're still the one running around in the middle. The logic of an Agent is: You tell me what you want, I'll handle it. Shift from humans adapting to tools, to tools adapting to humans. How Is This Different from Automation? Good question. Because the essence of automation is also "you just say it, the machine does it." The traditional automation

I got laid off in March 2026. The day HR handed me the 30-day notice, I had a small panic attack, then opened my laptop and started building things. Here's the deal: I had 30 days before severance ran out, and I wanted to see how much I could ship with AI tools before the money (and motivation) ran dry. I gave myself a single rule — every project gets a 7-day deadline, otherwise I kill it. I built 6 things. One has real users. One broke in production. Two I never opened again. This is what happened, in the order I built them. 1. AI Buddy (Chrome sidebar) — shipped, 15 users A Chrome extension that puts an AI assistant in a sidebar. Select text on any page, hit a keyboard shortcut, it goes to the AI, reply shows up without you leaving the page. Works with GPT-4, Claude, Gemini, DeepSeek. No login, no credit card. Time: 11 days (April 1–11). Status: Live on Chrome Web Store. 15 real users as of June 28, 2026. Rating 4.2. What I used AI for: 90% of the code (500 lines of JavaScript, written in Cursor). The README, the Chrome Web Store description, the marketing tweets — all AI-drafted, then I rewrote the parts that sounded like AI. What went wrong: The first version had a Stripe integration. AI wrote 90% of the webhook signature verification. I had to rewrite it from scratch. Also the model-picker UI went through 5 revisions because AI kept proposing what looked right but didn't work. → Chrome Web Store 2. Weekly report generator — personal use only Every Friday at 4pm, a script grabs my git commits, Slack messages, and Linear ticket changes, throws them at GPT-4, and asks for a "manager-readable" weekly report. I review, tweak, send. Time: 2 days. ~200 lines of Python. Status: Running for 11 weeks. Has 1 user. Me. Cost is $0.12/week. What I used AI for: The prompt. It's surprisingly tricky to get GPT-4 to write a weekly report that doesn't sound like a robot. The single most useful line: "if you don't have data, write 'no progress this week' — don't make things up." T

Labeled break/continue is now live across the entire Cx language stack. From the lexer to the JIT, two commits streamlined the implementation into a complete vertical slice. No parts left out, no corners cut—everything just works. The lexer seam If you're handling character literals and labels in the same codebase, you'll know what a pain this can be. We opted for a Rust-like syntax: 'ident for labels, with no closing quote. The label regex sits right after LiteralChar in the logos enum. Thanks to longest-match, 'x' becomes a char literal, while 'outer is parsed as a label. Escape sequences? They can't match labels because they have a backslash, making 'x' always look cleaner in code. A test fixture ( t_char_literal_guard ) ensures this order and regex integrity hold. Change the lexer rules, and it'll catch any mistakes immediately. Two-commit split We didn't cram this into a single mega-commit. No, we split it into two. Commit f94c6a5 laid down the frontend groundwork—adding the Label token, allowing loops and breaks to carry optional labels, and rejecting any misuse with semantic checks. The interpreter and JIT, however, initially took a back seat, guarding themselves against mislabeled jumps. Then came commit 0f56f1e , which wiped out these guards and enabled real execution on backends. Now all parses and semantic checks play nicely without rogue labeled breaks sneaking into the wrong loops. Interpreter changes The interpreter now handles BreakSignal and ContinueSignal carrying labels. Loops catch these signals when the label is either absent (defaulting to the innermost loop) or matches their own. The difference from before? Zero unlabelled break/continue behavior change while facilitating outward jumps. JIT changes The JIT saw more extensive adjustments, gaining a label field within LoopContext . Push and pop that context on a stack, trace it through lowering calls, and you've got labeled jumps pinpointed. The unlabeled jumps? They get the same treatment as bef

Circuit breakers and bulkheads are design patterns — their numbers are operational weapons. Failure ratio 50% or 30%? Max concurrent calls 25 or 100? During an outage the right answer changes hourly . Code the pattern once; tune thresholds live . Kiponos.io separates resilience structure (in Java) from resilience parameters (in live config tree). Pattern in code, numbers in Kiponos public boolean allowCall ( String downstream ) { var cfg = kiponos . path ( "resilience" , downstream ); return breaker ( downstream ) . failureRateThreshold ( cfg . getFloat ( "failure_rate_threshold" )) . waitDurationInOpenState ( cfg . getInt ( "open_seconds" )) . permittedInHalfOpen ( cfg . getInt ( "half_open_calls" )) . tryAcquire (); } Ops opens circuit sensitivity during brownout — dashboard edit, not redeploy. Resilience tree resilience/ payments-api/ failure_rate_threshold : 0.5 open_seconds : 30 half_open_calls : 5 bulkhead_max_concurrent : 40 inventory-api/ failure_rate_threshold : 0.35 open_seconds : 60 bulkhead_max_concurrent : 25 global/ force_open_all : false Extreme: coordinated degradation Platform SRE sets force_open_all: false normally. During regional disaster, flip selective open_seconds sky-high on non-critical downstreams — bulkhead by configuration , Java still executes pattern logic. Performance Breaker checks are per-call — getFloat() must be local. See rate limits article . Getting started Externalize Resilience4j YAML values to resilience/* Incident drill: tighten failure_rate_threshold live Resources: github.com/kiponos-io/kiponos-io Kiponos.io — resilience patterns with live numbers. Breakers that bend during the outage.

you've felt it. you type a prompt, hit send, and the response starts streaming in under a second....