Finance CRM agent fleet for invoices, collections, client health, and cash flow.Ops Lead routes everything. Humans approve. Shared knowledge base keeps it tight.

I've been working on Blazorly, an open-source coding harness, and I'm excited to share it with you! It's built around making coding agents more reliable, with durable sessions, better interruption handling, and a chat UI or CLI that runs on your own machine. No hosted control plane. Your machine, your keys, your workspace. I'd love to hear what you think! github.com/deepakkumar1984/b…

I'M A DEVELOPER. I'M BUILDING AN AI STARTUP WITH AI. I SHIP FEATURES IN MINUTES. What could possibly go wrong?

Insurance and IT Cell sit on the same card. Two dimensions, two sets of tags, one work item. That card lives in a workspace with a board, list, timeline, calendar, and workload. The budget stays on the page when you switch. The write-up is a page, and a share link opens it with no account. Hours are a timer on the item, billable or not. The assistant drafts the client status. It does not send the email. Start free: synqtag.com

SynqTag's assistant can draft a client status from the workspace. It waits. The board stays as it is until you approve the change. I want the draft. I want to be the one who hits save. Does the AI on your task tool wait, or does it just go? synqtag.com

I got tired of the board in one tool, the write-up in another, and the hours in a spreadsheet. SynqTag is what I built so those sit together. Free for one person. If you run client work, where do the hours actually get written down? synqtag.com

Blazorly Harness now supports per-workspace agent configuration. Instructions: Custom instructions saved per workspace, with a fixed identity header for harness, provider, model, and date. Tool allow-list: Enforced across existing chats, new chats, CLI runs, and subagents. Workspace isolation: Each workspace has its own configuration, stored in workspaces.json — never in global settings. Dynamic placeholders: {{provider}}, {{model}}, {{cwd}}, {{date}}, and {{weekday}} are interpolated automatically. Configure your agent once per workspace. Keep instructions and tool access scoped exactly where they belong. harness.blazorly.com/ #Blazorly #AI #DevTools

Tons of new features and made the tool calls efficient which reduces token usage and made it faster. Its a full blown editor style harness.blazorly.com/

Blazorly Harness 0.1.9 Some calls in an agent aren't generation.... they're judgment. Does this brief need a plan before anything changes? Could this tool call destroy data or leak secrets irreversibly? Those were answered by regexes and counters. Now there's a decision seam, backed by System One: unstructured state in, typed decisions out (Choice / Score / Noul), one parallel pass, no generated text. Two seams live, auto-plan and risk-gate. Off by default, and off means exactly the old behaviour. On, it's escalate-only: allow → ask, never ask → allow, never softens a deny, and it stands down headless. Timed out, errored or uncertain all fall back to the deterministic logic - a decision model can never be the reason a turn fails. github.com/deepakkumar1984/b… @typesafeai

I’ve been working on Blazorly Harness, an open-source coding-agent runtime for .NET 10. The interesting part isn’t another chat UI. It’s the infrastructure underneath it: → Agent loop with tools, planning and subagents → MCP + LSP support → Terminal, filesystem, web search and code tools → Local models through Ollama / LM Studio → OpenAI, Anthropic, DeepSeek, xAI and compatible APIs → Sandboxed execution with Landlock or E2B → Persistent sessions with SQLite / JSONL → Session fork, resume, archive and export → Context compaction and token tracking → Skills, workflows, hooks and project instructions → Background jobs and agent teams → REST, WebSocket, JSON-RPC and ACP → CLI + Blazor web UI → Plugin architecture → Built-in evaluation and benchmark tooling → Windows, macOS and Linux I’m building it as something you can actually run, automate and extend — not just another wrapper around an LLM. github.com/deepakkumar1984/b… #DotNet #OpenSource #AIAgents

Now Oracle has just downsized again. For the second time this year. Large corporations everywhere are following suite to spend even more on their AI data centers. These data centers are not taxed. And a lot of these corporations are not paying taxes themselves anyway. Thus the state loses income tax and corporate tax at the same time. However, there is one more complication. Who will be able to afford something when machines are doing all the work? Data centers neither rent flats nor buy groceries. And what is going to happen to all those who once had a salary? UBI and robot tax are still only ideas. And hoping that new jobs will appear is why we are in this situation. Now we are spending unbelievable sums to replace people working for the economy. This will only work as long as you neglect the source of income after they disappear.

I’m genuinely trying to understand these AI benchmarks. Every month there’s a new model that scores higher than the previous one. I’ve been using Claude, Kimi, DeepSeek, MiMo 2.5 etc for actual development, and honestly I’m not seeing what these benchmark scores are supposed to mean for normal software work. When I give them an issue to fix, most of the time they fix the thing I pointed out. But they rarely go, “wait, this could also affect these other parts” or proactively check other possible causes, edge cases, side effects, etc. That’s the kind of thing I actually want from a coding model. So what are these benchmarks really measuring from a software development perspective? If the scores keep going up, why doesn’t it feel like the models are getting proportionally better at actually doing software engineering? Maybe these benchmarks are much more useful for things like building 3D models, games, solving specific coding problems, etc. But for day to day software development, I’m struggling to see how a higher benchmark score translates into a better developer experience.

A candle store spent the year automating its way to a bigger loss. Revenue held at $3,200 a month. Costs climbed to $4,100. The stack behind it was impressive: a Mac Mini running agents, Claude drafting, n8n workflows watching competitor pricing around the clock, listings adjusted overnight. The machinery behaved exactly as designed. Revenue did not move. Costs did, and then some. Data-driven loss, the post about the shop called it. AI-powered loss. The owner's wife just wanted more wicks. Nothing failed, which is the uncomfortable part. The agents were reliable, the data was fresh, the pipeline ran unattended for months. It was finely tuned equipment bolted onto a business that was not making money before the machines arrived, and the machines cost money to run. Optimisation is a multiplier. When the margin is negative, it multiplies the loss first. The same week, a lawn care owner with three trucks posted about an AI receptionist that lets him answer every call and stay on the tools. That automation sits where the money already is, so every call it catches is a booking won. The difference was never the model. It was whether the task under the automation had revenue on the other end. We start scoping calls the same way now: what is the task worth per hour, and who collects the value. If nobody can answer, the tooling is just a faster way to lose money. Optimised loss is still loss. #BusinessAutomation #CustomSoftware #AIDevelopment #OperationalExcellence #AustralianTech #DeeshaTech #Leadership #Automation

Last week, a new tool called Ballet hit Hacker News offering to write integrations against any API. The question is less important than the responses from operators of these systems once they are up and running. A few comments mentioned that good demo systems had good APIs; the bad ones were moving targets. Others said that the hard part was stated more plainly: where did the automation live, who was watching it, and how would you keep it running? This is the pain that businesses feel on Tuesday mornings. The upstream system modifies a field. The connector loses access. The retry duplicates an invoice rather than correcting the first. The integration might still look great in a demonstration video. As we develop our Power Platform flows and custom operational software, the fact that it works once proves that the two systems can communicate. It does not prove that the system will survive change. A system needs an owner who can spot an error and fix it without generating more work under the covers. Integration is getting easier. Boredom is where the value lies.

Could an AI become superintelligent without ever becoming conscious... and if so, does consciousness actually matter for intelligence?

OpenRouter leaderboard right now: Chinese models: 8 out of top 10 GPT: “I’m still here though…” (barely) The takeover isn’t coming. It’s already logged 100+ trillion tokens.

One family. Two clear tracks. Blazorly is no longer just an AI app builder. The product family now covers the application lifecycle and the agent lifecycle, with each surface responsible for a specific job. blazorly.com/