How do AI spreadsheets work?

Sparkco AI transforms natural language into powerful spreadsheets instantly. Just describe what you need in plain English, and our AI agents build formulas, charts, pivot tables, and connect your data sources automatically. No manual Excel work required.

What data sources can I connect?

Connect to databases (PostgreSQL, MySQL, MongoDB), SaaS tools (Stripe, QuickBooks, Salesforce), EHR systems (PointClickCare, Epic), cloud storage, and REST APIs. Our AI automatically syncs and analyzes your data in real-time.

Is Sparkco AI secure for sensitive data?

Yes. Sparkco AI is fully HIPAA compliant and SOC 2 Type II certified. We maintain enterprise-grade security with data encryption, access controls, and regular audits. BAA available for healthcare customers.

How is this different from Excel or Google Sheets?

Traditional spreadsheets require manual formula building and data entry. Sparkco AI builds everything automatically from natural language, connects live data sources, and provides intelligent analysis. It's like having an expert analyst build spreadsheets for you in seconds.

Can I use this for healthcare operations?

Yes. Sparkco AI provides specialized healthcare solutions including patient referral screening, admissions automation, and voice-powered EHR documentation. Our agentic EHR infrastructure transforms skilled nursing facility operations.

How quickly can I get started?

Start building AI spreadsheets immediately - no setup required. For healthcare solutions, most facilities are operational within 2-4 weeks including EHR integration and staff training.

AI infrastructure, tools, and open research.

Name: Sparkco AI Spreadsheet Agent
Brand: Sparkco AI

Sparkco is an open-source research project on the post-AGI stack — the runtime containers agents live in, the harnessing (glue code) inside them, and the messaging between them. It's built by the team behind Ancha, where we build simulation environments for testing AI agents against stateful mock APIs, people, and timing. The site is our public log of that work: research, a live AI feed, and tools for agent builders.

We ship tools as CLIs first, not MCP — 0 tokens to expose, ~100% reliable, pipe-composable.

LiveLast item 10h ago

7 in feedRSSAncha

Research direction · April 2026

Parametric memory: replacing the context window with weights.

Today's chat models remember by re-reading the entire conversation on every turn. Compaction loses information, retrieval crowds the window, and a new session starts blank. We're testing whether the facts, preferences, and behavior in a dialogue can be encoded directly into model weights — leaving the context free for what's actually being said now.

Iteration 1–2: negative.TTT fast weights tested first; trained perturbations did not encode retrievable memory under a single-GPU budget.ttt-conv-memory

Want to collaborate? patrick@ancha.io

Read the full direction

The problem

The context window is a finite token sequence, fully recomputed on every turn. Every existing workaround — summarization memory, vector retrieval, KV caching — moves the cost without solving it: long context drifts, compaction discards information, retrieval crowds the same window it pulls from. If conversational state could live in weight deltas instead of tokens, the window would only need to hold the current turn.

Four directions in the wild

Test-time training. ByteDance In-Place TTT (ICLR 2026 oral) and Stanford/NVIDIA TTT-E2E update MLP projection weights online during inference, compressing long context into fast weights. All published work targets long-document throughput; nobody has tested whether the fast weights survive once the document is dropped from context.
Hypernetwork → adapter. Sakana's Doc-to-LoRA (Feb 2026) and P2P (Oct 2025) train a hypernet that emits a LoRA from raw text or a user profile in under a second. Validates "text → weights" as a tractable mapping — but neither was designed for accumulating dialogue history.
Dialogue-direct fine-tuning. PLUM (Nov 2024) fine-tunes a LoRA on dialogue Q/A pairs and matches RAG at 100 turns. MemLoRA trains memory management itself as a LoRA. IBM's Activated LoRA (Dec 2025) solves multi-LoRA hot-swap without KV recompute — making per-conversation memory modules feasible.
Knowledge editing. ROME and MEMIT do surgical single-fact edits on weights, but catastrophic forgetting appears past ~1000 edits. Not a candidate at dialogue scale.

The gap

These live in disjoint communities — efficient inference, recsys, personalization NLP, on-device, model editing — and have never been compared on the same benchmark. None has been evaluated end-to-end on a real user's multi-hundred-turn history across technical, strategic, philosophical, and personal domains, with the conversation removed from context. Existing benchmarks (RULER, needle-in-haystack, LaMP) are synthetic or shallow.

What we're testing

TTT fast weights as memory. Ingest a fact-bearing dialogue with In-Place TTT, drop the context, probe. Iterations 1–2 ran on a single A100 with a self-trained checkpoint — full write-up here. Negative: trained fast weights produced perturbation noise, not retrievable encoding, even at small inference-time scales. Joint base+TTT training is the next attack surface.
Doc-to-LoRA over real dialogues. Same probes, hypernet-generated LoRA instead of TTT. Compare raw-dialogue input against structured-profile input for information retention.
Modular memory adapters. Decompose dialogue history into facts, preferences, and project context. Train one LoRA per axis; hot-swap with Activated LoRA. Measure single-load vs combined-load interference.
Capacity and forgetting curves. Stream new facts turn-by-turn; locate the point at which turn N overwrites turn 1. Trace the capacity–fidelity tradeoff.

Expected output

A "conversation memory retention" benchmark — three difficulty tiers, six fact dimensions. None currently exists for this scenario.
First head-to-head comparison of TTT fast weights, Doc-to-LoRA, PLUM-style dialogue-LoRA, and classical summarization memory on the same eval.
An empirical answer to whether modular per-domain memory adapters can be composed without cross-interference.

The landscape

Three layers, and what's already out there.

Containers

Sandboxes, microVMs, durable runtimes — where the agent lives.

e2b
Code-interpreter sandboxes; the default for general-purpose runs.
Modal
gVisor + GPU-native; sub-1s starts, scales to 50k+ concurrent.
Daytona
Open source; ~90–200ms cold start, fastest in class.
Fly.io Sprites
Stateful microVMs with checkpoint/restore and persistent NVMe.
Vercel Sandbox
Firecracker + idle-billed; the JS-stack default.

Ancha

Ancha runs your agent against stateful mock APIs. Container mode restricts network access to Ancha and the model hosts you allow.

Harnessing

Glue code inside the container. Context curation, tool routing, the runtime loop.

Claude Agent SDK
Anthropic's harness; powers Claude Code itself.
Inspect AI
Eval-grade harness used by METR, Apollo, and government AISIs.
LangGraph
LangChain's runtime layer — durable execution, threads, HITL.
Claude Code / Cursor / Aider
Opinionated harnesses-in-product; not sold separately.

Ancha

Ancha works with your existing agent and harness. Start from a production trace or a scene file, then check tool state and the order of actions.

Messaging

Between containers. Discovery, identity, stateful tasks — not tool-calling.

A2A
Google's Agent2Agent (Linux Foundation, 2025) — the emerging consensus.
ANP
Peer-to-peer agent network over HTTPS + DIDs for identity.
Letta
Shared memory blocks + thread-based message passing.
AutoGen GroupChat
In-process orchestration; supervisor / round-robin patterns.

Ancha

Ancha lets you script replies, timing, and faults across tools such as Slack and GitHub, with one shared clock and starting state.

Open source

What we ship publicly.

github.com/spfunctions

Harness & agents

Curated lists

Terminal tools

Signals & probability

World-state plumbing

SDK adapters

Test your agent with Ancha

Build repeatable tests with stateful mock APIs, simulated people, and checks on what your agent actually did.

Live feed

Latest research and agent-testing guides from Ancha and Sparkco.

Refreshes every 5 min

Blog

AI Agent Evaluation: Check Tool State, Not Just the Answer

Evaluate tool-using agents with state, trace, and process checks. See how Ancha adds repeatable environments to an existing eval workflow.

What Ancha believes about testing AI agents: what your agent does stays done, your evals already have a home, everything that happens is an event, grade the end state, never a silent 200.

Ancha5d ago

Featured · 8

What we'd install on a fresh machine

Three of ours, five from the community we trust.

Ancha

Sparkco

Test AI agents with stateful mock APIs, simulated people, and repeatable scenarios.

npm install -g @ancha-labs/cli

TypeScript

@spfunctions/harness

Sparkco

Dual-agent runtime. Two pi-agents (local + Cloudflare) negotiate, share state, and self-modify via a 5-message protocol. $1/day to run.

npm i -g @spfunctions/harness

TypeScript

LangChain

Most adopted agent framework. Chains, tools, memory.

pip install langchain

126kPython

OpenHands

Autonomous software engineer. Code, test, deploy.

pip install openhands

50kPython

Browser Use

Open-source browser agent library. See, click, type.

pip install browser-use

50kPython

Aider

Git-aware AI pair programmer in your terminal.

pip install aider-chat

25kPython

@spfunctions/bi

Sparkco

Agent-friendly BI CLI. Query CSV/JSON/Parquet with SQL via DuckDB. 4 commands: head, schema, query, convert.

npm i -g @spfunctions/bi

TypeScript

DuckDB

In-process SQL OLAP database. Parquet, CSV, JSON out of the box.

brew install duckdb

Most adopted agent framework. Chains, tools, memory.

pip install langchain

126kPython

OpenHands

Autonomous software engineer. Code, test, deploy.

pip install openhands

50kPython

Browser Use

Open-source browser agent library. See, click, type.

pip install browser-use

50kPython

MetaGPT

Multi-agent software company simulation.

pip install metagpt

45kPython

CrewAI

Role-based multi-agent crews with delegated tasks.

pip install crewai

45kPython

GitHub CLI (gh)

Repos, PRs, issues, Actions — all from the terminal.

brew install gh

37kGo

AutoGen

Multi-agent conversations by Microsoft Research.

pip install autogen

35kPython

Cline

VS Code extension with terminal access. Autonomous coding.

code --install-extension saoudrizwan.claude-dev

30kTypeScript

DuckDB

In-process SQL OLAP database. Parquet, CSV, JSON out of the box.

brew install duckdb

28kC++

Aider

Git-aware AI pair programmer in your terminal.

pip install aider-chat

25kPython

DSPy

Programming, not prompting. Optimized LM pipelines.

pip install dspy-ai

20kPython

SWE-Agent

Resolves real GitHub issues autonomously.

pip install swe-agent

15kPython

OpenAI Agents SDK

Official OpenAI agent framework with function calling.

pip install openai-agents

15kPython

Vercel CLI

Deploy, manage, and inspect Vercel projects.

npm i -g vercel

12kTypeScript

pgcli

Postgres CLI with auto-complete and syntax highlighting.

pip install pgcli

12kPython

Skyvern

Vision-driven browser navigation for AI agents.

pip install skyvern

10kPython

Pydantic AI

Type-safe agent API built on Pydantic.

pip install pydantic-ai

10kPython

dbt

SQL-first data transformation framework. ELT best practices.

pip install dbt-core

10kPython

xsv

Fast CSV toolkit. Index, search, split, join, stats.

cargo install xsv

10kRust

Datasette

Instant JSON API and web UI for any SQLite database.

pip install datasette

9.5kPython

Miller (mlr)

Like awk, sed, cut for CSV, TSV, and JSON. Stream processing.

brew install miller

9.0kGo

usql

Universal SQL CLI. Postgres, MySQL, SQLite, DuckDB, 40+ drivers.

go install github.com/xo/usql@latest

9.0kGo

Mastra

TypeScript-first agent framework with built-in tools.

npm i mastra

8.0kTypeScript

VisiData

Terminal spreadsheet multitool. Explore, sort, filter, pivot.

pip install visidata

8.0kPython

gogcli

Google Workspace CLI — Docs, Sheets, Drive, Calendar.

brew install gogcli

6.7kGo

csvkit

Suite of CLI tools for converting to and working with CSV.

pip install csvkit

6.0kPython

Stripe CLI

Payments, webhooks, and Stripe resource management.

brew install stripe/stripe-cli/stripe

5.0kGo

Smolagents

HuggingFace minimal agents. Simple, composable.

pip install smolagents

5.0kPython

OctoSQL

Query CSV, JSON, Parquet, MySQL, Postgres with a single SQL.

go install github.com/cube2222/octosql/cmd/octosql@latest

5.0kGo

hledger

Plain text double-entry accounting. Scriptable, composable.

brew install hledger

4.4kHaskell

Playwright MCP

Playwright browser automation exposed as MCP tools.

npx @anthropic/playwright-mcp

3.0kTypeScript

prediction-market-analysis

Largest public dataset + analysis framework for prediction markets.

git clone https://github.com/nweii/prediction-market-analysis

2.8kPython

ElevenLabs CLI

TTS, STT, voice cloning, and audio generation.

pip install elevenlabs

2.0kPython

sqlite-utils

CLI for manipulating SQLite. Insert, transform, query, extract.

pip install sqlite-utils

2.0kPython

pmxt

CCXT for prediction markets. Unified API across exchanges.

pip install pmxt

1.4kPython

Supabase CLI

Database, Auth, Storage, and Edge Functions management.

brew install supabase/tap/supabase

1.0kGo

Polymarket Agents

Official Polymarket agent framework for autonomous trading.

git clone https://github.com/Polymarket/agents

800Python

kalshi-ai-trading-bot

AI trading bot for Kalshi with Grok-4 integration.

git clone https://github.com/elizaOS/kalshi-ai-trading-bot

342Python

polymarket-mcp-server

45-tool MCP server for Polymarket data and trading.

git clone https://github.com/berlinbra/polymarket-mcp-server

335TypeScript

polybot

Reverse-engineer strategies, find arbitrage on Polymarket.

git clone https://github.com/polybot-nexus/polybot

200Python

PredictOS

All-in-one multi-agent framework for prediction markets.

git clone https://github.com/PredictOS/predictos

200Python

dr-manhattan

CCXT for prediction markets with market-making support.

pip install dr-manhattan

181Python

polyterm

Polymarket in your terminal. Browse and trade from CLI.

cargo install polyterm

162Rust

CloddsBot

Autonomous AI agent trading across 1000+ markets.

git clone https://github.com/CloddsBot/cloddsbot

158Python

polymarket-pipeline

Event-driven AI news-to-trade pipeline for Polymarket.

git clone https://github.com/polymarket-pipeline/pipeline

154Python

gnosis/prediction-market-agent

AI agent that bets on prediction markets to benchmark reasoning.

git clone https://github.com/gnosis/prediction-market-agent

150Python

kalshi-trading-bot-cli

AI-native Kalshi CLI with Kelly sizing and risk management.

git clone https://github.com/kalshi-trading/bot-cli

149Python

kalshi-python

Official Kalshi Python SDK for market data and trading.

pip install kalshi-python

100Python

gnosis/pm-agent-tooling

Benchmark and deploy prediction market agents.

pip install prediction-market-agent-tooling

100Python

crm-cli

CLI customer relationship management. Contacts, deals, notes.

pip install crm-cli

72Python

AgentMail CLI

Email inboxes and transactional email for AI agents.

pip install agentmail

Python

Know one we're missing? Submit it →

Blog · 6

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