# Praxist Is Free Until You Gross US$1 Million a Year, and Its $3,054 Benchmark Bill Ran on a DeepSeek Price That Ended August 16

Sapient's Praxist loop read from the paper, the license and the setup wizard: a US$1M gross revenue gate, a cost tied to a retired DeepSeek price, and a narrow small-business fit.

Author: J.A. Watte
Published: September 11, 2026
Source: https://jwatte.com/blog/praxist-sapient-autonomous-research-fair-source/

---

The decision in front of you is not whether Praxist is interesting. It is whether to give a two-week-old repository a Linux box, an API key with your card behind it, and forty-odd hours of unattended runtime. The paper's "roughly a twelfth of the cost" rests on a token ledger in which 96.0% of all input was billed at DeepSeek's cache-hit price (my arithmetic on Appendix B.1, [arXiv 2608.25955](https://arxiv.org/html/2608.25955)), the one price DeepSeek raised sixfold at 16:00 UTC on August 16, 2026 ([DeepSeek changelog](https://api-docs.deepseek.com/updates)). The repository was created August 27 and had 6,561 stars and 627 forks at about 17:00 UTC on September 11, from 13 commits by five contributor accounts, one PyPI release (0.5.0) and zero GitHub Releases ([GitHub API](https://api.github.com/repos/sapientinc/PRAXIST), [commits API](https://api.github.com/repos/sapientinc/PRAXIST/commits?per_page=13), [PyPI](https://pypi.org/pypi/praxist/json)). A launch-day post from the company's account said "Starting today, PRAXIST is open source" ([X timeline](https://syndication.twitter.com/srv/timeline-profile/screen-name/Sapient_Int)). The README's FAQ answers the heading "Is Praxist open source" with "The precise description is source-available" ([README Q8](https://github.com/sapientinc/PRAXIST/blob/main/README.md)), and the license you accept by installing it is free only while you and your affiliates gross under US$1,000,000.00 a year ([LICENSE.md 1.3.1 and 1.3.2](https://github.com/sapientinc/PRAXIST/blob/main/LICENSE.md)). I read the paper, the license clause by clause, the wizard's source and the September bug reports, in that order, before the README's FAQ.

## What Praxist is, and what has to exist before it will start

The README calls Praxist "an autonomous research system for measurable, computer-executable research" that "treats research as a persistent process rather than a sequence of disconnected prompts" ([README](https://github.com/sapientinc/PRAXIST/blob/main/README.md)). The mechanism: you hand it a project that already runs and already produces a number you want moved, and it spends hours or days running rounds of parallel agents that edit the project, score each edit through a fixed evaluator, keep a ranked frontier of what worked, and start the next round from the winners. It is "not a replacement for Codex"; the coding agent stays interactive and Praxist adds "the persistent research loop, parallel peers, evidence protocols, scheduling, and lifecycle control" (same README).

The vocabulary, in plain English:

- **Peers**: the agents in one round. 8 in the ML template, 12 per task in the paper's MLE-bench run, 16 in the rocket run ([task.yaml](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/templates/tasks/machine_learning_template/task.yaml), [paper](https://arxiv.org/html/2608.25955)).
- **Generations**: the rounds, each with a time window (2.0 hours and 20 generations maximum in the ML template). In the paper's MLE-bench run and its rocket run, the top 4 artifacts of each round parent the next ([paper](https://arxiv.org/html/2608.25955)).
- **Evaluator**: one public command the task owns, normally `evaluations/<name>/run.py`, scoring by "the task's own metric, never the agent's self-assessment" (paper). Every metric must declare `maximize` or `minimize`; "Unknown direction remains unknown; reports do not guess that it should be maximized" ([task-projects.md](https://github.com/sapientinc/PRAXIST/blob/main/docs/guides/task-projects.md)).
- **Evidence maturity**: a ladder from smoke checks through scout probes to a complete scored evaluation; a high score at a preliminary rung "cannot crowd out a complete result during promotion" (paper).
- **Lanes**: confirmed (mature enough to parent), candidate, diagnostic and validation, all task-declared; the trading campaign declared an "alpha incubator" lane.
- **DIG**, the Deep Innovation Gate: read-only planning before any code, one committed mechanism contract per peer. "DIG is not an experiment loop. It does not train, evaluate, write variants, or encode task metrics" ([deep-innovation-gate.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/deep-innovation-gate.md)). By default it runs only before generation zero.
- **QD**: one candidate per peer from its own DIG pool, with caps per mechanism family so twelve peers do not all try the obvious thing ([qdig-cohort-allocator.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/qdig-cohort-allocator.md)). The paper expands it as "Quantified Diversity", the docs as "Quality-Diversity".
- **Gems**: compressed lessons kept across a memory reset, off by default; only the trading campaign used them and the rocket run "produced no Gems" (paper).

Before any of that, `first-task.md` requires six things: research code with a normal entrypoint, a runtime that imports the dependencies, data or a simulator "locally reachable through the project's normal interface", a baseline that "runs without Praxist", a measurable objective whose "direction is known", and constraints you can state ([first-task.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/first-task.md)). Then the sentence that defines the product's honesty boundary: "Takeover reports missing prerequisites instead of downloading an unknown dataset, inventing a simulator, or fabricating baseline performance." FAQ Q7 adds that Praxist "does not guarantee a specific metric improvement"; what a run that misses still leaves you is "a negative-result evidence package, an audit report, and recommendations on whether to stop or redirect", which is what the token bill buys on a bad week ([README](https://github.com/sapientinc/PRAXIST/blob/main/README.md)).

The install is one line, verbatim from `installation.md`. Codex:

```
python3 -m pip install --index-url https://pypi.org/simple "praxist[agents,codex]" && praxist setup --interactive --install-skills codex
```

Claude Code:

```
python3 -m pip install --index-url https://pypi.org/simple "praxist[agents,codex]" && praxist setup --interactive --install-skills claude
```

Both stop after readiness checks without choosing a project or launching anything, and the same page says to verify with `praxist --version`, `praxist doctor` and `praxist examples list` ([installation.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/installation.md)). One independent install took 10.3 seconds into a clean Python 3.12 venv and `praxist doctor` ran in 0.08 seconds ([WhatJustShipped](https://whatjustshipped.com/praxist-gained-5800-stars-in-five-days-and-it-is-not-open-source/)), so the install is not where the hours go; and doctor is a readiness check, not an auth test, since issue #3 records it returning ok while the Codex login then failed ([#3](https://github.com/sapientinc/PRAXIST/issues/3)). At the root of a runnable project you invoke `$praxist-takeover` in Codex or `/praxist-takeover` in Claude Code; the shell form is

```
praxist --takeover --task-path /absolute/path/to/research-project
```

with `--operator claude` for Claude Code ([first-task.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/first-task.md)). Takeover "inspects readiness, creates or repairs the task harness, validates its evaluator and evidence contract, and launches the run after the required gates pass"; then `praxist status --json` reports, `praxist --monitor --latest` watches, and "Ctrl-C closes only the monitor; it does not stop the research run" ([README](https://github.com/sapientinc/PRAXIST/blob/main/README.md)), which matters the night you go to bed with a metered process running. The command that does stop it is `praxist stop <run_id>`, and `praxist resume <run_dir>` picks a stopped run back up (same README); learn both before the first run, because a run launched with `--daemonize` outlives the agent conversation that started it ([platform-support.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/operations/platform-support.md)).

## What the paper shows, and what the press release says it shows

Table 2 gives Praxist 60 medals of 75 (80.0%), 49 gold, against 55 (73.3%) and 34 gold for its Claude Code baseline ([arXiv HTML](https://arxiv.org/html/2608.25955)). Four things sit around that table, all stated by the authors. The arms ran different models: "Praxist runs deepseek-v4-pro with a 1M-token context window as its research-agent model on all 75 competitions"; "The baseline runs Claude Opus 4.8 with maximum thinking budget" (§3.2), so the 12x cost gap is partly a model-price gap, and cost figures "provide resource context only". It is one sweep, "single-sweep outcomes rather than variance estimates over repeated sweeps", and "we therefore do not place them beside the public MLE-bench leaderboard", which requires at least 3 seeds as mean ± SEM, closed to submissions on 04-24-2026, and whose main table tops out at 64.44 ± 1.18 (a 77.78 ± 0.44 entry sits in a separate section the maintainers mark as not directly comparable) ([MLE-bench README](https://github.com/openai/mle-bench/blob/main/README.md)); its canonical compute is one 24 GB A10 per task, while both arms here shared an H100 80GB pool. Raw scores went the other way: on the 70 tasks both arms scored, Claude Code had the better raw score on 36 and Praxist on 33, so the advantage "lies in which side of a leaderboard threshold a run lands on, not in winning the majority of head-to-head score comparisons". And the ledger was adjudicated internally: 90,423 attempts rejected, 85,960 under an "integrity_policy" the paper never defines, nine tasks with a lineage swapped for a "clean actor's submission", and an "audit correction of 26 August 2026", the submission date, that "settled that ledger at 49 gold" (Appendices B.1 and C).

The press release, datelined Palo Alto, August 28 and on the wire at 01:00 UTC that day, leads with "49 gold-medal outcomes across 75 machine learning competitions from MLE-Bench achieving approximately US$3,000, versus 34 for Claude Code at US$38,000", attaches "controlled internal evaluations" to that paragraph only, and introduces the rocket and SLAM items as "Partner engineering results" ([Business Wire release via Yahoo Finance](https://sg.finance.yahoo.com/news/sapient-intelligence-launches-praxist-beta-010000334.html)). The four case studies, where the paper hedges three headline wins and puts a selection caveat on the fourth:

| Case study | Sapient, publicly | The paper |
|---|---|---|
| Rocket landing | "100% within 12 hours" at "Technology Readiness Level (TRL) 3" (press release); landing page "+82.88% ... 17.12 → 100" | 100% by generation 9 of a run SIGTERMed during generation 12 after 14.35 hours, no 12-hour figure; the +82.88% is against Weco's 17.12%, not the 4.03% baseline; a frozen simulator, banks that "carry selection-overfitting risk", "the measured rates are not real-world landing reliability"; controller "selected post-run rather than promoted as a committed frontier artifact" |
| Quant trading | Landing page "+30.2% CAGR 22.8 → 53.0" | The 53.07% artifact was chosen "post hoc, as the highest walk-forward CAGR recorded anywhere in the campaign; it is not the campaign's own promoted artifact"; incubator lane, "three hard constraint violations", one seed over 29 cells; the confirmed-lane winner was a different policy with lower CAGR |
| SLAM | "a partner team achieved 9.37cm ... PRAXIST reduced the error to 5.01cm within three days" (press release); About page "median relative reduction of 49.2%" | Baseline is "the stock FAST-LIVO2 implementation as built for this campaign"; the snapshot spans 145 h 52 min; "We deliberately do not report that gap as an accuracy improvement", since re-associating timestamps under one rule "leaves the mean relative APE change at -0.09% rather than the +45% the raw columns suggest"; the supported claim is 72.4% less VIO processing time; runs picked "without a pre-declared acceptance rule" |
| Fusion control | About page: baseline is "a replica of the deployed MAST-U plasma control system" | Baseline is "an architectural reconstruction rather than a port" with gains "inherited from the Praxist controller"; it completes more episodes (11 of 15 vs 10) and wins the original full-horizon metric (4.42 vs 4.65); "none of the three controllers is classified as passing under the official rule"; champion "first appears in generation 0" |

Sources: paper §3.3 to §3.6 and Appendices B.2 to B.5; the press release; the [Praxist About page](https://praxist.sapient.inc/en/about); the [landing page](https://praxist.sapient.inc/en). The About page also describes a wind test to 20 m/s that the paper's audit did not run; the no-wind boundary belongs to that audit.

The introduction concedes the wider point: Praxist "improves the task-native baseline's headline metric in two domains, leads on survival but not on full-horizon precision in tokamak control, and, in the fourth, cuts the baseline's visual-compute cost at unchanged accuracy". AI Scientist, AlphaEvolve and FunSearch are cited and never raced, and AIDE appears only as the tree search underneath Weco; Weco, the one other system compared, is "an external autonomous-optimizer reference rather than a controlled cross-model comparison" because it ran a different model, claude-opus-4-6, on a different clock ([Weco's dashboard](https://dashboard.weco.ai/share/xZJAbpYNN7aJCvyiZuuZFsp_hzQdPqTy)). The repo ships none of the MLE-bench, trading, SLAM or fusion harnesses; the Drive release holds six zips, no fusion archive, an mle README reading "in_progress (finalize not executed)", and a jobshop archive the paper never discusses ([Drive folder](https://drive.google.com/drive/folders/1xNQ6mI8Q2WynzWr3i7RdI4FevNHrn1gs)). I found no replication or third-party audit of any number across the repo's 42 issues and PRs, Hacker News or Reddit; what shipped is two examples of one rocket problem and four templates.

## The $3,054 depends on a DeepSeek price that ended on August 16

Now the ledger itself. Appendix B.1 records 110,910,266,304 cache-hit input tokens, 4,602,586,778 cache-miss input tokens and 1,108,180,075 output tokens for the 75-task run, priced at CNY 0.025 per million cache-hit input, 3 cache-miss and 6 output on deepseek-v4-pro (0.02, 1 and 2 on v4-flash, which served part of the volume): CNY 20,694.84 in total, "approximately US$3,054" at the Federal Reserve H.10 rate of 6.7766 on 10 July 2026 ([Appendix B.1](https://arxiv.org/html/2608.25955)). That is where the 96.0% cache-hit share comes from (110.9 billion of 115.5 billion input tokens, my arithmetic). Those were real list prices; an archived copy of DeepSeek's pricing page from 10 July 2026 shows exactly them ([Wayback, 10 July 2026](http://web.archive.org/web/20260710082518/https://api-docs.deepseek.com/zh-cn/quick_start/pricing/)). On August 13 DeepSeek announced peak and off-peak pricing effective 16:00 UTC on August 16, and V4 Pro's off-peak list became CNY 0.15, 4.5 and 13.5: 6x the paper's cache-hit price, 1.5x its cache-miss price, 2.25x its output price ([changelog](https://api-docs.deepseek.com/updates) for the date, [Chinese pricing page](https://api-docs.deepseek.com/zh-cn/quick_start/pricing) for the CNY figures, which the changelog itself does not list). The live page on September 11 reads $0.022, $0.66 and $1.98 off-peak per million, double at peak (01:00 to 04:00 and 06:00 to 10:00 UTC, weekdays) ([DeepSeek pricing](https://api-docs.deepseek.com/quick_start/pricing)). That 30x gap between a hit at $0.022 and a miss at $0.66 is the cost story in one number.

Re-pricing the ledger is a price-list exercise, not a cost prediction: it assumes the same token volume and the same 96.0% cache-hit rate on whatever model sits in the row. Every figure below is my arithmetic, with every token on v4-pro (some ran on flash, so true figures would be a little lower):

| Price schedule applied to the paper's token ledger | Total | Against the paper's US$3,054 |
|---|---|---|
| DeepSeek V4 Pro, July 2026 list (what the paper used) | about US$3,054 | 1x |
| DeepSeek V4 Pro, September 11 off-peak list | about US$7,672 | about 2.5x |
| DeepSeek V4 Pro, September 11 peak list | about US$15,344 | about 5x |
| DeepSeek V4 Pro, September 11 off-peak, zero cache hits | about US$78,433 | about 25x |
| DeepSeek V4 Pro, July 2026 cache-miss rate on every token (about CNY 353,000) | about US$52,100 | about 17x |
| OpenAI gpt-5.6-luna, standard short-context list ($0.20 / $0.02 / $1.20) | about US$4,469 | about 1.5x |
| Anthropic Claude Opus 4.7 list ($5 / $0.50 / $25), misses billed as base input | about US$106,173 | about 35x |

Rates from [DeepSeek](https://api-docs.deepseek.com/quick_start/pricing), [OpenAI](https://developers.openai.com/api/docs/pricing) and [Anthropic](https://platform.claude.com/docs/en/about-claude/pricing). The cache-hit rate does most of the work: at today's off-peak list the cache discount removes about US$70,761 of input cost, and Praxist's own guides say "no model name alone guarantees a high cache-hit rate" and that it "does not promise a fixed token or billing reduction" ([open-source-model-apis.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/open-source-model-apis.md), [cost-optimization.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/cost-optimization.md)). At today's list the cheapest row is OpenAI's Luna, the model Praxist hard-codes for its no-API-key mode, on which I found no published run. The baseline's US$38,370 has no token breakdown, so it cannot be re-priced. In units a single business would buy: the paper's figure is US$40.72 per MLE-bench task against US$511.60 for the Claude Code arm (US$3,054 and US$38,370 over 75, my arithmetic), about US$102 per task at today's off-peak list and US$205 at peak, for tasks that ran 12 peers under a 24-hour wall cap on an H100 pool; and the only other campaign the paper puts a dollar figure on, in what I read, is the rocket run, US$196.05 for 16 peers over 14.35 hours on DeepSeek V4 Pro ([paper](https://arxiv.org/html/2608.25955)).

The recommended model is itself in flux. DeepSeek's September 10 release note says "We're phasing out V4-Pro" and routes all deepseek-v4-pro requests to V4.1-Flash from 04:00 UTC on September 14 ([release note](https://api-docs.deepseek.com/news/news260910)); the pricing footnote and the changelog say V4 Pro continues "with the billing method remaining unchanged" ([pricing](https://api-docs.deepseek.com/quick_start/pricing), [changelog](https://api-docs.deepseek.com/updates)), while the release note still carried the phase-out wording on September 11, so DeepSeek's own pages disagreed on the day I read them. Praxist's setup source pins its DeepSeek profile to `deepseek-v4-pro[1m]` ([setup.py](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/praxist/cli/setup.py)); the cheaper V4 Flash that served part of the paper's volume is one of the models the same changelog says "have been retired", with the old names temporarily routed to V4.1 Flash. On OpenRouter the obvious slug `deepseek/deepseek-v4-pro` is the older 0423 build served by third parties; DeepSeek's own list-price endpoint sits under `deepseek-v4-pro-0813` ([OpenRouter](https://openrouter.ai/api/v1/models/deepseek/deepseek-v4-pro-0813/endpoints)). The release's "approximately US$3,000" was a July number, at a July price, at a cache-hit rate the vendor's docs say no model name guarantees.

## The license, clause by clause, with the revenue clock

The file is titled "Fair Source License Agreement (Version 1.0)", the Licensor is Sapient Intelligence Pte Ltd, the Publication Date reads 08/28/2026, and it was committed at 2026-08-27T14:20:01Z ([LICENSE.md](https://github.com/sapientinc/PRAXIST/blob/main/LICENSE.md), [license commit](https://github.com/sapientinc/PRAXIST/commit/ba04667bd806ee63ddfc2417f72bd7d169d78a70)). GitHub's detector reports "Other" (SPDX NOASSERTION) and PyPI's `license_expression` is null ([GitHub API](https://api.github.com/repos/sapientinc/PRAXIST), [PyPI](https://pypi.org/pypi/praxist/json)). Section by section, quotations from [LICENSE.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/LICENSE.md):

| Section | What it says |
|---|---|
| 1.1.3 Annual Revenue | "worldwide aggregate gross revenue (not net profit or revenue after deduction of costs)" of you and your Affiliates; a company without a full fiscal year annualizes "by multiplying its average monthly revenue since its establishment by twelve (12)"; non-USD converts at the Reuters closing mid-rate |
| 1.1.4 Affiliate | any entity controlling, controlled by or under common control with you, at more than 50% of voting equity or by contract |
| 1.1.5 Generated Output | "any model parameter updates, model weight files, inference outputs or optimization results" |
| 1.1.6 Commercial License | "a paid license agreement to be separately executed" |
| 1.2.1 Grant | non-exclusive, non-sublicensable and non-transferable except as 1.2.3 and 1.2.4 allow: (a) use, copy, modify and (b) derivative works "for internal business purposes"; (c) "deploy and run the Software within its own organization, whether by way of online service or on-premises installation" |
| 1.2.3 Distribution | no distributing, selling or transferring the Software or a Modified Version "as a standalone product or service" without written consent; providing "the functionality of the Software to end users as part of its own services" is allowed if not sold as "Praxist by Sapient Intelligence" |
| 1.2.5 Output attribution | internal use needs nothing; output given to any third party must keep the product-name attribution "in a reasonably prominent position", and the duty "shall continue to apply even if the Licensee modifies, edits or otherwise reprocesses the output" |
| 1.3.1 Revenue Threshold | "One Million United States Dollars (USD $1,000,000.00)" |
| 1.3.2 Condition | valid only while below the threshold; on reaching it the free license "shall automatically lapse" and you must contact the Licensor within 30 days |
| 1.3.3 Clock | (a) written notice within 30 calendar days; (b) good-faith negotiation within 60 calendar days after that notice; (c) no agreement within 90 calendar days and the free license "shall automatically terminate", you "cease using the Software and delete all copies" |
| 1.3.4 Revenue reporting | every 12 months the Licensor may require "a revenue statement signed by an authorized representative", due in 30 calendar days; refusal permits termination. It is a self-certification; the agreement contains no books-and-records inspection right |
| 1.3.5 Dispute | a reasonable dispute over the revenue calculation freezes the status quo |
| 1.3.6 Academic exemption | universities, public research institutes and non-profit academic institutions, "solely" for their own teaching, research and non-profit publication; a for-profit subsidiary, spin-off, tech-transfer platform or fee-based service loses it |
| 1.4 Data collection | product-usage collection "is not a condition of the license"; installing or using is not consent |
| 1.5 Restrictions | no use "for military, surveillance or large-scale human rights violations"; no reverse engineering except where law forbids contracting out |
| 1.6 IP | all IP "including ... patent rights" stays with the Licensor; no patent grant anywhere; you grant Sapient "a perpetual, irrevocable, royalty-free, worldwide license" to patches you submit, the only "irrevocable" in the file |
| 1.7 Warranty | AS IS; "The Software is an AI model tuning tool, and its output may contain bias, errors or inaccuracies" |
| 1.8 Liability | capped at fees paid in the prior 12 months, or "USD $100.00" if use was free |
| 1.9.2 Breach | missing the 1.3.3(a) notice "constitutes an incurable material breach" with immediate termination; other breaches under 1.9.2 get a 30-day cure, but 1.3.3(c) and 1.3.4 terminate without one |
| 1.10 Law | Singapore law, then the Singapore International Arbitration Centre after "friendly negotiation"; "Checking the confirmation box for this Agreement shall be deemed acknowledgment and execution thereof" (1.10.4) |

The literal string "Praxist by Sapient Intelligence" appears in the preamble, Definition 1.1.1, Section 1.2.3 and README FAQ Q8, not in 1.2.5 itself, which says "product name attribution" and that the Licensor "may provide recommended attribution wording in writing"; I found no such wording anywhere public. A separate User Agreement (version 2026-08-28), also accepted in the wizard, carries a revision clause in its §1.4 under which continued use after notice is acceptance ([user-agreement.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/legal/user-agreement.md)); the license itself has no version-upgrade clause, no Change Date, and never mentions MIT, Apache, GPL or OSI.

A hypothetical, to show why 1.1.3 bites before the fiscal year does. A firm founded on March 1 that bills US$60,000 in March, US$90,000 in April and US$120,000 in May averages US$90,000 a month; times twelve is US$1,080,000 (my arithmetic), so it crossed the line in its third month of existence, before it has a fiscal year to close, and 1.3.3(a)'s 30-day window opened then; under 1.9.2 missing that window is incurable. The drafting never says use during the 60-day negotiation window is licensed: 1.3.2 says the free license "shall automatically lapse" on crossing, and only 1.3.5, a genuine dispute about the arithmetic, freezes the status quo. Two more things the text settles for a small operator. The Licensee is "you (an individual or legal entity)" (preamble), so a freelancer is inside the same revenue test on their own gross receipts, and the 1.3.6 carve-out names institutions, not students or hobbyists. And nothing public says what the Commercial License costs: the landing page lists three engagement models, "Strategy & Validation", "Applied Research" and "Enterprise Deployment", with no prices ([landing page](https://praxist.sapient.inc/en)), and the press release names no price, waitlist or hosted product ([press release](https://sg.finance.yahoo.com/news/sapient-intelligence-launches-praxist-beta-010000334.html)). That is a description of the text, not advice.

## Fair Source in name only

The project's own CONTRIBUTING.md says Praxist "is source-available under a non-OSI Fair Source license" ([CONTRIBUTING.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/.github/CONTRIBUTING.md)). The name suggests two things it is not.

Against the Open Source Definition ([OSD v1.9](https://opensource.org/osd)) it fails four criteria at once: no redistribution (criterion 1 against 1.2.3), derivatives for internal use only (criterion 3 against 1.2.1), a field-of-use ban plus a paid tier (criterion 6 against 1.5 and 1.3), and a "separately executed" Commercial License (criterion 7 against 1.1.6). Mapping the revenue cutoff to criterion 5 is my reading.

Against fair.io's definition ([fair.io/about](https://fair.io/about/)): Fair Source Software "is publicly available to read; allows use, modification, and redistribution with minimal restrictions to protect the producer's business model; and undergoes delayed Open Source publication (DOSP)", meaning publication "under an Open Source (OSI-approved) license", which the site calls "a bright line" and its FAQ times at about two years ([fair.io/faq](https://fair.io/faq/)). Praxist's license has no Change Date, no conversion, and bars redistribution; Sapient is not among fair.io's 13 listed companies and its license is not among the three fair.io names ([fair.io/licenses](https://fair.io/licenses/)). What it resembles is the earlier thing fair.io says "would not be considered Fair Source Software by today's definition": the 2015 Fair Source License from Sourcegraph, free per entity up to a set user count, which Kyle Mitchell's 2016 first read found "does not meet The Open Source Definition" ([Mitchell, March 2016](https://writing.kemitchell.com/2016/03/30/First-Read-of-the-Fair-Source-License)). Swap headcount for gross revenue, drop the 2015 text's redistribution and patent grant, and you have Praxist's structure.

| License | Redistribute the code | Open-source conversion | What gates free use | Hosting inside your own product |
|---|---|---|---|---|
| Praxist Fair Source License Agreement 1.0 | No (1.2.3) | None | Gross revenue under US$1M including affiliates (1.3) | Allowed unless sold as "Praxist by Sapient Intelligence" (1.2.3) |
| FSL-1.1-MIT (Sentry) | Yes, for any non-competing use | Yes, "effective on the second anniversary", under MIT | A "Competing Use" | Allowed unless it competes |
| Business Source License 1.1 | Yes | Yes, on the Change Date, at most the fourth anniversary of each version | Production use beyond the Additional Use Grant: "purchase a commercial license" or "refrain from using" | Depends on the Additional Use Grant |
| Elastic License 2.0 | Yes | None | No revenue or time gate | Not as a hosted or managed service exposing "any substantial set of the features" |
| 2015 Fair Source License (abandoned) | Yes: the archived v0.9 text grants a copyright and patent license to "use, copy, distribute, prepare derivative works of" the software, subject to the Use Limitation | None | Users per entity | Not addressed in what I read |

Sources: [FSL-1.1-MIT template](https://raw.githubusercontent.com/getsentry/fsl.software/main/FSL-1.1-MIT.template.md), [BUSL 1.1](https://mariadb.com/bsl11/), [Elastic License 2.0](https://www.elastic.co/licensing/elastic-license), [Mitchell](https://writing.kemitchell.com/2016/03/30/First-Read-of-the-Fair-Source-License). FSL also grants "a license under our patents" with defensive termination, and even the abandoned 2015 license granted rights "under all of the Licensor's copyright and patent rights" ([archived 2015 fair.io](http://web.archive.org/web/20151124190446/https://fair.io/)); Praxist grants none. The same clause-by-clause method on a different license is in [Julia: What MIT Actually Gave Away, and What JuliaHub Will Not Put a Price On](/blog/julia-juliahub-what-mit-gave-away/).

## Running it: a ChatGPT plan or an API key, and what the September 1 bug reports say about the bill

The wizard offers exactly four profiles ([setup.py](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/praxist/cli/setup.py)):

| Profile id | Label and description in the wizard | Provider / runtime | Model | API key |
|---|---|---|---|---|
| codex-native | "Codex-native mode": "saved Codex login; best for short or exploratory runs" | openai / codex_sdk | gpt-5.6-luna | No |
| deepseek-api | "DeepSeek API (recommended)": "cost-efficient long research through claude_sdk" | deepseek / claude_sdk | deepseek-v4-pro[1m] | Yes |
| openrouter-api | OpenRouter API | openrouter | anthropic/claude-opus-4.7 by default | Yes |
| anthropic-api | Anthropic API | anthropic | claude-opus-4-7 | Yes |

Those four are all there are, despite the quickstart's mention of "Other supported API-backed setup profiles"; `_env.py` recognizes kimi, moonshot, qwen, mistral, groq and xai only for hand configuration ([quickstart.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/quickstart.md), [_env.py](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/praxist/cli/_env.py)). Two of those labels matter for the bill. The README recommends Codex and says "No API key is required in Codex-native mode; Praxist uses your authenticated Codex session" ([README](https://github.com/sapientinc/PRAXIST/blob/main/README.md)); the wizard calls that profile best for short or exploratory runs, the quickstart says "For sustained, cost-sensitive research, prefer an open-source model API", and the paper contains zero occurrences of Codex, ChatGPT, OpenAI or Luna ([paper](https://arxiv.org/html/2608.25955)). The model is fixed at gpt-5.6-luna, with Praxist's default effort `max` mapped to Codex `xhigh` ([agent-runtimes.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/agent-runtimes.md)); OpenAI describes Luna as its "lowest cost in the family" model ([OpenAI models page](https://learn.chatgpt.com/docs/models)).

What a ChatGPT plan buys. OpenAI lists Plus at $20 a month and Pro "From $100", beside an "API Key" option "Great for automation in shared environments like CI." ([OpenAI pricing](https://learn.chatgpt.com/docs/pricing)). Its usage table gives Luna "250-2,000" local messages per five-hour period on Plus and "5,000-40,000" on Pro 20x, with "Weekly limits may also apply."; OpenAI's credit card rates Luna at 5 credits per million input tokens, 0.5 per million cached and 30 per million output, says "GPT-5.6 usage averages 5-30 credits per message", and lets Plus and Pro users who hit the limit buy more credits, but no page I opened prices a credit in dollars or states credits per plan, so the allowance cannot be turned into generations; on that card the paper's ledger would be about 111,700 Luna credits (my arithmetic) ([OpenAI pricing](https://learn.chatgpt.com/docs/pricing)). Pro 20x sign-ups were paused on September 10 ([OpenAI help](https://help.openai.com/en/articles/9793128-about-chatgpt-pro-plans)). The SDK and `codex exec` are available on Plus and Pro, "Codex access tokens for trusted automation" only on Business and Enterprise, and the auth page says "API keys are still the recommended default for automation." ([OpenAI auth docs](https://learn.chatgpt.com/docs/auth)). The Terms of Use prohibit circumventing rate limits and sharing credentials and never address unattended SDK runs ([Terms of Use](https://openai.com/policies/terms-of-use/)); I am not claiming a breach, and Praxist's User Agreement puts the question on you: credentials' "ownership and permitted use are governed by the User's agreement with the relevant provider" ([user-agreement.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/legal/user-agreement.md)). The help article on credits does not price them either ([help article](https://help.openai.com/en/articles/12642688)). The trade is exposure: in Codex-native mode there is no key to overrun, and the runtime "never falls back to an API or relay when Codex-native mode was selected" ([credentials.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/credentials.md)), so the worst case is a stalled run at the plan's limit; what an API key buys is the re-pricing table above and an open meter, and Praxist's costs guide sends you to "API provider invoices" for the actual bill ([costs.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/costs.md)).

The September 1 bug reports. Issues #81 through #85 came from one account and one run: praxist 0.5.0 at commit aa198d0, macOS 27.0 on arm64, Python 3.14 ([issues API](https://api.github.com/repos/sapientinc/PRAXIST/issues?state=all&per_page=100&sort=created&direction=asc)), outside the release-tested matrix of Linux on CPython 3.11 and 3.12 ([ci.yml](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/.github/workflows/ci.yml)), on what the report itself describes as "agent_runtime:codex_sdk via the run-scoped relay to an OpenAI-compatible provider". So these are one operator's numbers from one route on an untested platform, and still the only third-party ones:

- [#81](https://github.com/sapientinc/PRAXIST/issues/81): a run granted 500,000 tokens and 1,260 seconds "consumed roughly 6,000,000 tokens over 1,737 seconds", about 12x, with a `budget_overrun` record written after the fact. Part of this is documented design: "Budget is dynamic in Praxist. It is not a fixed tuple copied once into a run and then blindly enforced for every experiment", and "The default posture is result preservation" ([budget-policies.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/budget-policies.md)).
- [#83](https://github.com/sapientinc/PRAXIST/issues/83): a GPU-scheduled harness on a CPU-only host admitted zero evaluation jobs with no error while "Peers keep generating candidate variants and consuming millions of tokens against a queue that will never evaluate anything".
- [#85](https://github.com/sapientinc/PRAXIST/issues/85): Praxist "discards the provider's cost metering; no neuron count and no monetary figure appears anywhere in run artifacts."

[#82](https://github.com/sapientinc/PRAXIST/issues/82) reports a live run shown as `stale`. On September 11 all five were open, four with zero comments, none with a maintainer reply. PRs #109 (budget gating) and #111 (cost metering) were closed on September 3 into a branch `validation/budget-and-cost-accounting`, unmerged on September 11; the maintainer's closing note says the PR was closed "only because its contribution has been transferred" to that branch, "not because the contribution was rejected" ([PR #109](https://github.com/sapientinc/PRAXIST/pull/109)), and the contributing guide says such validation "commonly takes one to three days" ([CONTRIBUTING.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/.github/CONTRIBUTING.md)). So nobody, inside or outside Sapient, has published a cost-accounted run of the shipped code: the one independent test, a Medium post from August 30, gives a 2M-token budget and no dollar figure ([Medium post](https://medium.com/@rohanprichard/i-tried-praxist-by-sapient-labs-on-an-ml-dataset-ce6baecfa6ae)); WhatJustShipped installed the package but ran no research loop ([WhatJustShipped](https://whatjustshipped.com/praxist-gained-5800-stars-in-five-days-and-it-is-not-open-source/)); and I did not read the 1,413-member Discord ([Discord invite API](https://discord.com/api/v9/invites/sapient?with_counts=true)).

Time is the other bill. The ML template ships cohort 8, 20 generations and 2.0 hours each, a configured 40-hour ceiling ([task.yaml](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/templates/tasks/machine_learning_template/task.yaml)); the rocket example's server harness fixes 90 minutes per generation and suggests 30 generations, 45 hours ([task_GPU_server resource_plan.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/examples/rocket_booster_recovery/task_GPU_server/assets/resource_plan.md)). The MLE-bench and rocket numbers came from H100 80GB pools ([paper](https://arxiv.org/html/2608.25955), [resource_plan.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/examples/rocket_booster_recovery/task_GPU_server/assets/resource_plan.md)); I found no equivalent hardware line for the SLAM or fusion campaigns, and the rocket example's task_PC variant ships null baselines that "have never been measured on a personal computer" ([rocket README](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/examples/rocket_booster_recovery/README.md)). No document states a minimum CPU, RAM or disk; a co-founder's claim to a Chinese self-media outlet that the theoretical minimum runs on one laptop comes with no task, machine or result ([NetEase Hao](https://m.163.com/dy/article/L6FV5P8H05118UGF.html)).

## What a technical small business could point it at, and what will not fit

The ML template says what it is for in three bullets: "supervised or self-supervised ML projects with a measurable prediction or model-output artifact"; "projects that already have runnable code and a credible evaluator or validation protocol"; "task initialization through the `praxist-task-initialization` skill" ([ML template README](https://github.com/sapientinc/PRAXIST/blob/main/templates/tasks/machine_learning_template/README.md)). Its evaluator interface is `run.py --prediction-artifact PATH --output PATH`, and the shipped file raises SystemExit until you replace it ([run.py](https://github.com/sapientinc/PRAXIST/blob/main/templates/tasks/machine_learning_template/evaluations/primary/run.py)); its baselines README says "Do not invent baseline numbers" ([baselines README](https://github.com/sapientinc/PRAXIST/blob/main/templates/tasks/machine_learning_template/assets/baselines/README.md)). The paper's applicability sentence is "wherever a task admits an executable evaluator: model and algorithm development, controller synthesis for physical systems, simulation-driven engineering design, and quantitative strategy research", with "slower or noisier evaluators" as future work ([paper](https://arxiv.org/html/2608.25955)).

Two fits I can ground.

**A model already in a notebook with a held-out metric.** This is the one independent run. On the Adult Income tabular set with a logistic-regression baseline and a 2M-token budget, the agents could change preprocessing and feature engineering only; degree-two feature interactions beat the baseline on each of the five development runs the author configured, some preprocessing changes and a feature-selection approach came out worse, and the author writes "This is not a claim that Praxist has solved machine learning research" ([Medium post](https://medium.com/@rohanprichard/i-tried-praxist-by-sapient-labs-on-an-ml-dataset-ce6baecfa6ae)). A churn or lead-scoring model in a notebook with a held-out AUROC is the honest counterpart: known metric direction, data on disk, a baseline that runs in seconds, and an agent scope narrower than the whole problem.

**A scheduling or pricing heuristic with a cost function over replayable data.** This is my reading of the contract, not a vendor-documented use. The first-task table accepts "Training, optimization, simulation, inference, or evaluation" as the baseline path that must run without Praxist ([first-task.md](https://github.com/sapientinc/PRAXIST/blob/main/docs/getting-started/first-task.md)); the task layout wants "one public command such as `evaluations/<name>/run.py`" ([templates README](https://github.com/sapientinc/PRAXIST/blob/main/templates/README.md)); task initialization runs a one-unit canary through that command before wider execution, and the metric must declare `minimize` or `maximize` ([task-projects.md](https://github.com/sapientinc/PRAXIST/blob/main/docs/guides/task-projects.md)). A replayable cost function behind one command, direction minimize, satisfies every line. The only scheduling artifact Sapient has released is heavier: a 4.7 GB `jobshop.zip` in the paper's Drive folder for a "Dynamic Flexible Job-Shop Direct Rescheduling" task that the paper never mentions ([Drive folder](https://drive.google.com/drive/folders/1xNQ6mI8Q2WynzWr3i7RdI4FevNHrn1gs)), and the landing page's "Job-Shop Optimization +2.0% EFFICIENCY 1.4 → 3.4" card links no run lineage ([landing page](https://praxist.sapient.inc/en)). So the fit is real in the contract and unproven in public; a small manufacturer with an OR-Tools scheduler and a year of order history would write the evaluator, the baseline records and the task.yaml itself.

Sapient's own picture of the business fit sits on its landing page as metric cards: "Automatic case/document/ticket tagging" micro F1 0.60685 to 0.79589, "Next-best-product recommendations" MAP@12 0.02177 to 0.03344, "Real-time abuse screening" AUROC 0.77842 to 0.95897 ([landing page](https://praxist.sapient.inc/en)). Each is a Kaggle-shaped metric on a prediction artifact, the ML template's fit restated, and none links a run, so read them as the shape the vendor has in mind rather than as results. Whatever you point it at, the license's warranty clause says "The Licensee should independently verify results prior to deploying any model tuned through the Software" (1.7).

What does not fit, by the vendor's own gates: anything scored by humans; live traffic without a replay; a process with no simulator; a metric whose direction you cannot declare; a baseline that does not yet run on its own. Agents "must not download, install, or provision" missing data, packages or simulators during a run ([templates README](https://github.com/sapientinc/PRAXIST/blob/main/templates/README.md)). The harness is not small: the only template with a real evaluator, `sam_optimizer`, is ResNet-18 on CIFAR and needs "a compatible CUDA runtime", and its size is the best gauge of what "write the evaluator yourself" means: a 26,719-byte task.yaml, a 22,722-byte evaluator and a 71,222-byte benchmark harness, plus six role files ([sam_optimizer README](https://github.com/sapientinc/PRAXIST/blob/main/templates/tasks/sam_optimizer/README.md)). The templates README lists what a "serious task project" carries: task.yaml, README.md, description.md, prompt_task.jinja2, roles/, audit_rules/, evaluations/, and assets/ for the harness, baselines, literature and regression fixtures ([templates README](https://github.com/sapientinc/PRAXIST/blob/main/templates/README.md)); and the examples README says "Examples are not authoring scaffolds", so the rocket project is for study, not for copying into your task ([examples README](https://github.com/sapientinc/PRAXIST/blob/main/examples/README.md)). The Python rocket example's verified path "requires Python 3.11, a CUDA-capable GPU, and a driver compatible with JAX CUDA 12"; its PC variant ships null baselines and, per open issue #189, fails its canary on a fresh install on one stale frozen-hash pin, still unfixed on main on September 11 ([rocket README](https://github.com/sapientinc/PRAXIST/blob/main/examples/rocket_booster_recovery/README.md), [#189](https://github.com/sapientinc/PRAXIST/issues/189)). And if what you publish is the model itself, 1.1.5 defines weights and optimization results as Generated Output, and 1.2.5 attaches the attribution duty to "any output generated by the product" that reaches a third party (it does not use the defined term), so on the plain reading the model card carries it.

## Before pip install: Linux only, a key in the run directory, and telemetry that is off until you say yes

The platform matrix ([platform-support.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/operations/platform-support.md)):

| Platform | Status in the docs |
|---|---|
| Linux on CPython 3.11 or 3.12 | "Continuously tested by release CI" |
| macOS on CPython 3.11+ | "Package and CLI compatibility target; not continuously tested by release CI" |
| Linux on other CPython 3.11+ versions | "Package compatibility target; not continuously tested by release CI" |
| Windows-native | "Outside the current research-runtime contract"; "Use a supported Linux environment instead" |

WSL appears nowhere in the four documents that cover platform support (that matrix, installation.md, quickstart.md and the README); a repository-wide search would not run for me, so I cannot say what the code itself does on a WSL kernel. The strongest supportable statement is: Windows-native is outside the contract, and WSL is undocumented either way. Codex-native support adds "roughly 100-150 MB" ([installation.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/installation.md)), and the keyring fix for Codex-native logins (issue #3, PR #10, merged August 31) is on main only, since PyPI's sole release dates from August 27 ([PyPI](https://pypi.org/pypi/praxist/json), [#3](https://github.com/sapientinc/PRAXIST/issues/3)).

The quickstart's agent-managed setup opens with `codex --yolo` or `claude --dangerously-skip-permissions` ([quickstart.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/quickstart.md)); both flags tell the coding agent to stop asking before it acts, and the runbook then forbids it from accepting legal terms on your behalf or asking for an API key in chat. The interactive wizard runs legal, privacy ("Nothing is preselected"), runtime and readiness stages, with keys typed at a masked prompt.

Two data flows leave the box. The big one is the agents' model traffic: every API-key profile sends it to the provider you picked in the wizard, under your key, DeepSeek's API in the recommended profile, and non-OpenAI providers reached through the Codex runtime pass through a run-scoped "codex-relay" first ([credentials.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/credentials.md)); nothing in the docs I read keeps any of it local. Codex-native routes it through your ChatGPT login instead, which Praxist stages "inside a private disposable OS-temporary Codex home" and records only as a hash of the account identifier (same guide). The small flow is telemetry, and that is better than the README makes it sound. README FAQ Q5 says Praxist "collects only limited system-level operational information, which you can disable at any time", which reads as default-on; the Privacy Notice (version 3) and license 1.4 say nothing is collected while consent is "unset" and accepting the license is not consent ([PRIVACY.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/legal/PRIVACY.md), [LICENSE.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/LICENSE.md)). With consent, four event types (run_started, generation_finished, run_finished, run_reconciled) carrying a random `environment_id` stable across runs and bounded error counts, and explicitly no task content, prompts, keys, model names or hardware, go to `https://telemetry.theaiscientist.com/v1/events`, a different domain from sapient.inc. Events are kept up to 180 days; the notice says the server is managed PostgreSQL in Johor, Malaysia; and the server "does not offer an interface to delete already-delivered events by environment identifier". The collector ships in the repo under the `product-usage-server` extra ([pyproject.toml](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/pyproject.toml)). Check and withdraw with:

```
praxist product-usage status --json
praxist product-usage withdraw
```

"Not collected" is not "never written to disk". Issue #84 reports that on every API-key relay route the peer runtime writes the provider key in plaintext into `runtime_state/.../shell_snapshots/*.sh` inside the run directory and "Nothing in the run lifecycle scrubs it"; the thread traces it to a `shell_snapshot=false` override applied only on the subscription branch, so the relay path (deepseek, openrouter, mistral, groq, xai, moonshot, qwen) never gets it, and fix PR #196, opened September 10, was unmerged on September 11 ([#84](https://github.com/sapientinc/PRAXIST/issues/84)). The reporter frames it as a local artifact gap, not a remote vulnerability. The quickstart promises the raw key is never placed in "the command line, shell history, agent conversation, or task project" ([quickstart.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/quickstart.md)), and FAQ Q5 says keys are "not exposed in commands, shell history, or conversations" ([README](https://github.com/sapientinc/PRAXIST/blob/main/README.md)); the run directory is a different place, which is where it lands, and `praxist uninstall` plus `pip uninstall` never removes run artifacts ([installation.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/installation.md)).

## How to read a Sapient launch: the HRM episode and two weeks of stars

The press release introduces Sapient as the company behind the Hierarchical Reasoning Model, and that history is the best guide to how a Sapient number ages. The HRM paper (arXiv 2506.21734, v1 June 26, 2025) said a 27M-parameter model trained on about 1,000 examples "achieves a performance of 40.3%" on ARC-AGI ([HRM paper](https://arxiv.org/html/2506.21734v3)). Seven weeks later, on August 15, 2025, ARC Prize ran it on the hidden semi-private sets: 32% on ARC-AGI-1 for $148.50 and 2% on ARC-AGI-2 for $201, adding "we do not consider this material progress" ([ARC Prize](https://arcprize.org/blog/hrm-analysis)). Its ablations found a same-size plain transformer within about 5 points, the outer refinement loop adding 13, and training on the 400 evaluation tasks alone still reaching 31% against 41%, which ARC Prize read as memorization of those tasks' demonstration pairs rather than data leakage. In October 2025 a 7M-parameter follow-up reported 45% and 8% ([TRM](https://arxiv.org/abs/2510.04871)). In fairness: HRM shipped Apache-2.0, has 12,637 stars, and reached 339 points on Hacker News ([HRM repo API](https://api.github.com/repos/sapientinc/HRM), [HN](https://hn.algolia.com/api/v1/search?query=%22hierarchical%20reasoning%20model%22&tags=story)). The lesson is not that the lab was wrong; it is that the third-party number arrived seven weeks after the viral one and was lower. Praxist has no third-party number yet; the "300 to 500 research findings per run" line now circulating is a co-founder's workload guess, caveated as such by the outlet that printed it ([NetEase Hao](https://m.163.com/dy/article/L6FV5P8H05118UGF.html)).

The star curve, as observed:

| When (UTC) | Stars | Forks | Source |
|---|---|---|---|
| Aug 28, 02:01, about 12 hours after creation | 131 | 3 | Wayback |
| Sep 1, 07:48 | 5,690 | 494 | Wayback |
| Sep 2, 11:37 | 6,766 | 558 | Wayback |
| Sep 3, 21:51 | 6,846 | not recorded | tracker |
| Sep 5 | 6,301 | not recorded | tracker |
| Sep 10, 01:21 | 6,249 | not recorded | tracker |
| Sep 11, about 17:00 | 6,561 | 627 | GitHub API |

Sources: Wayback [Aug 28](https://web.archive.org/web/20260828020114/https://github.com/sapientinc/PRAXIST), [Sep 1](https://web.archive.org/web/20260901074831/https://github.com/sapientinc/PRAXIST), [Sep 2](https://web.archive.org/web/20260902113717/https://github.com/sapientinc/PRAXIST); [RepositoryStats](https://repositorystats.com/sapientinc/praxist); [GitHub API](https://api.github.com/repos/sapientinc/PRAXIST). The live count on September 11 was 6,561, 205 below the September 2 capture, while forks rose to 627; the tracker's series is a sawtooth with a Sep 3 high, and watchers rose steadily to 670. GitHub's per-user stargazer history would not open for me for any repository I tried on September 11, so no star-by-star timeline exists, and no source I read explains the pattern. I attribute no cause.

What the surge did not come from: Hacker News, where two August 28 submissions got 1 point and 0 comments each, both from accounts created August 21, one matching a co-author trailer on the initial commit ([HN Algolia](https://hn.algolia.com/api/v1/search?query=%22praxist%22&tags=story&advancedSyntax=true)); Reddit, where the one r/LocalLLaMA thread sits at 0 points with "buy an ad" on top ([thread](https://www.reddit.com/r/LocalLLaMA/comments/1w0k4yr/praxist_from_sapient_intelligence/), which would not open for me directly; I read it through a copy); or GitHub Trending Python, where two daily archives never list the repo ([trending archive](https://raw.githubusercontent.com/larsbijl/trending_archive/master/2026-09/2026-09-02.md)). The repo's own tracker is as quiet: 12 issues, and 2 discussions that have 0 comments between them ([issues API](https://api.github.com/repos/sapientinc/PRAXIST/issues?state=all&per_page=100&sort=created&direction=asc)). What the surge did come with: Sapient's launch post at 285.8K views as displayed on X on September 11 ([launch post](https://x.com/Sapient_Int/status/2093141554500338019)); a launch-day post from a large AI-commentary account carrying X's "Paid partnership" label, restating the medal numbers with no testing; quote-posts from accounts whose bios advertise promotion; a launch video at 74,343 views on a 1.34K-subscriber channel whose next upload drew 637 ([YouTube](https://www.youtube.com/watch?v=8BcUyd6s_Ek)); and a paid press release. WhatJustShipped called the star count "a measurement of curiosity about a paper" ([WhatJustShipped](https://whatjustshipped.com/praxist-gained-5800-stars-in-five-days-and-it-is-not-open-source/)).

The decision rule is short. Wait for a number from someone Sapient does not pay, the way ARC Prize produced one for HRM. Until then Praxist fits you only if you already meet the six prerequisites in `first-task.md`, have a Linux box (and an H100-class GPU if your evaluator needs one), can write the evaluator and baseline records yourself, and gross under US$1,000,000 including affiliates. Set the spending cap with your API provider rather than with Praxist, whose budget is documented as dynamic rather than enforced and whose run artifacts record no cost, starting, as the README's own FAQ puts it, "with a small representative workload before scaling up" ([README](https://github.com/sapientinc/PRAXIST/blob/main/README.md)). And read the revenue line the way 1.1.3 writes it: gross, including your parent, annualized in your first year, with a 30-day clock that starts the day you cross it.

## Related reading
- **[Julia: What MIT Actually Gave Away, and What JuliaHub Will Not Put a Price On](/blog/julia-juliahub-what-mit-gave-away/)**: the same clause-by-clause method applied to a different license.
- **[How a Small Business Runs AI Agents Without a $47,000 Surprise Bill](/blog/blog-ai-agent-cost-controls-smb/)**: the provider-side spend caps that issue #81 and the cache-hit dependence make necessary.
- **[How AI Agents Coordinate And How They Remember: The Two Architecture Choices Behind Every Agent Project](/blog/blog-ai-agents-coordinate-and-remember/)**: peers, generations, lanes and Gems are one project's answers to those two choices.
- **[GitHub's Top Repos of 2026 Are Mostly AI Agent Add-Ons. Here's What a Small Business Can Actually Do With Each One.](/blog/github-top-repos-2026-small-business/)**: where a 6,500-star, two-week-old repo sits against that list.

## Fact-check notes and sources
- **6,561 stars, 627 forks, 670 watchers, created 2026-08-27T14:20:58Z, license "Other" (NOASSERTION)**: GitHub REST API read about 17:00 UTC on 2026-09-11; a read at about 16:38 UTC gave 6,560 and 626, [api.github.com/repos/sapientinc/PRAXIST](https://api.github.com/repos/sapientinc/PRAXIST).
- **13 commits, five contributor accounts, one tag (0.5.0), zero GitHub Releases; 30 PRs of which 11 merged; 12 issues (8 open, 4 closed); 2 discussions with 0 comments**: GitHub API on 2026-09-11, [commits](https://api.github.com/repos/sapientinc/PRAXIST/commits?per_page=13), [pulls](https://api.github.com/repos/sapientinc/PRAXIST/pulls?state=all&per_page=100), [issues](https://api.github.com/repos/sapientinc/PRAXIST/issues?state=all&per_page=100&sort=created&direction=asc).
- **PyPI: one release, 0.5.0, uploaded 2026-08-27T23:02 UTC, requires_python >=3.11, license_expression null**: [PyPI JSON](https://pypi.org/pypi/praxist/json).
- **"Starting today, PRAXIST is open source" at 01:00:44 UTC Aug 28, the companion to the "Introducing PRAXIST Beta" launch post at 01:00:09 UTC, which had 285.8K views and 399 likes on Sep 11**: X statuses [2093141700185338065](https://x.com/Sapient_Int/status/2093141700185338065) and [2093141554500338019](https://x.com/Sapient_Int/status/2093141554500338019), read through the syndication timeline and in a logged-in browser; counts are X's own display.
- **README FAQ Q8 "source-available", Q7, Q5, "not a replacement for Codex", takeover sentence, Ctrl-C line**: [README.md on main](https://github.com/sapientinc/PRAXIST/blob/main/README.md), 2026-09-11. The README never literally says "not open source".
- **Token ledger, CNY 0.025/3/6 and 0.02/1/2, CNY 20,694.84, H.10 rate 6.7766 on 10 July 2026, US$3,054, baseline US$38,370, "roughly a twelfth", "resource context only"**: paper Appendix B.1 and §3.2, [arxiv.org/html/2608.25955](https://arxiv.org/html/2608.25955). The 96.0% share (96.02%, 110,910,266,304 of 115,512,853,082 input tokens) is my arithmetic.
- **Re-pricing table and the 30x hit-to-miss ratio**: my arithmetic on that ledger at [DeepSeek's Sep 11 list](https://api-docs.deepseek.com/quick_start/pricing) ($7,671.93 off-peak, $15,343.86 peak, $78,432.68 with zero cache hits, US$70,761 removed by the cache; $0.66 divided by $0.022 is 30), at the July cache-miss rate (about CNY 353,000, US$52,100), at [OpenAI's standard short-context list](https://developers.openai.com/api/docs/pricing) for gpt-5.6-luna ($4,468.54) and at [Anthropic's Opus 4.7 list](https://platform.claude.com/docs/en/about-claude/pricing) with misses as base input ($106,172.57). All rows assume every token on the row's model at a 96.0% cache-hit rate; the paper says part of the volume ran on the cheaper v4-flash. The per-task figures (US$40.72, US$511.60, about US$102, about US$205) are US$3,054, US$38,370, US$7,671.93 and US$15,343.86 divided by 75.
- **July 2026 DeepSeek prices were list prices**: Wayback capture of the Chinese pricing page on 10 July 2026 showing CNY 0.025/3/6, [web.archive.org](http://web.archive.org/web/20260710082518/https://api-docs.deepseek.com/zh-cn/quick_start/pricing/).
- **Peak/off-peak pricing from 16:00 UTC on 16 August 2026; "continue providing" footnote; V4 Flash "have been retired" with legacy names temporarily routed to V4.1 Flash**: [changelog](https://api-docs.deepseek.com/updates) and [pricing page](https://api-docs.deepseek.com/quick_start/pricing), read about 21:00 UTC on 2026-09-11.
- **"We're phasing out V4-Pro", reroute from 04:00 UTC Sep 14**: [DeepSeek release note, 2026-09-10](https://api-docs.deepseek.com/news/news260910), unchanged on Sep 11, so it disagreed with the pricing footnote and changelog that day. Re-check all three DeepSeek pages after September 14.
- **Praxist's DeepSeek profile pinned to `deepseek-v4-pro[1m]`**: [setup.py](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/praxist/cli/setup.py) on main, 2026-09-11.
- **OpenRouter: the deepseek/deepseek-v4-pro slug is the 0423 build on 16 third-party endpoints from $0.86 per million input; DeepSeek's own first-party endpoint at $0.66 / $1.98 / $0.022 sits under deepseek-v4-pro-0813**: [OpenRouter endpoints API](https://openrouter.ai/api/v1/models/deepseek/deepseek-v4-pro-0813/endpoints), 2026-09-11.
- **MLE-bench Table 2, different base models, single sweep, 36 vs 33 raw wins, 90,423 rejected attempts, nine clean-fallback tasks, "audit correction of 26 August 2026"; Table 26 top-4 promotion, 12 peers**: [paper HTML](https://arxiv.org/html/2608.25955), v1 submitted 26 Aug 2026 per [arxiv.org/abs/2608.25955](https://arxiv.org/abs/2608.25955). Self-reported; no replication found.
- **MLE-bench leaderboard: 3 seeds, closed 04-24-2026, top entry 64.44 ± 1.18, one 24 GB A10 for 24 hours, Praxist absent**: [openai/mle-bench README](https://github.com/openai/mle-bench/blob/main/README.md), 2026-09-11.
- **Rocket, quant, SLAM and fusion figures with the paper's boundary sentences; Weco 793 candidates, 2 h 37 min, US$1,009.66; Praxist US$196.05, SIGTERM at 14.35 h**: paper §3.3 to §3.6 and Appendices B.2 to B.5; Weco's dashboard confirms 793 experiments, 2 h 37 m and claude-opus-4-6 but shows no dollar figure, [dashboard.weco.ai](https://dashboard.weco.ai/share/xZJAbpYNN7aJCvyiZuuZFsp_hzQdPqTy).
- **Press release wording and framing**: Business Wire release read through the [Yahoo Finance Singapore copy](https://sg.finance.yahoo.com/news/sapient-intelligence-launches-praxist-beta-010000334.html); datelined August 28, 2026, Business Wire ID stamped 20260827, published 01:00 UTC Aug 28, marked as a paid press release. The businesswire.com page would not open for me.
- **About page claims (49.2% median APE reduction, "replica of the deployed MAST-U", wind test to 20 m/s)**: [praxist.sapient.inc/en/about](https://praxist.sapient.inc/en/about), self-reported.
- **Landing page deltas, including the "Job-Shop Optimization +2.0% EFFICIENCY 1.4 → 3.4" card, with no run lineage linked**: [praxist.sapient.inc/en](https://praxist.sapient.inc/en), 2026-09-11.
- **Drive release: six zips, no fusion archive, mle README "in_progress (finalize not executed)", jobshop.zip at 4.7 GB for a "Dynamic Flexible Job-Shop Direct Rescheduling" task; the paper has zero hits for job-shop, jobshop, rescheduling or makespan**: [Drive folder](https://drive.google.com/drive/folders/1xNQ6mI8Q2WynzWr3i7RdI4FevNHrn1gs); central-directory reads, archives not downloaded whole. The landing-page match is by subject only.
- **License clauses 1.1.1 through 1.10.4**: [raw LICENSE.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/LICENSE.md), 16,026 bytes, identical across blob, raw and local copies; added in commit [ba04667b](https://github.com/sapientinc/PRAXIST/commit/ba04667bd806ee63ddfc2417f72bd7d169d78a70) at 2026-08-27T14:20:01Z. "irrevocable" occurs once, "patent" once, "patent license" never; no inspection or audit right appears anywhere in the file. The annualization example is hypothetical and my arithmetic on 1.1.3's formula.
- **User Agreement version 2026-08-28, §1.4 revisions, provider-terms clause**: [user-agreement.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/legal/user-agreement.md).
- **"non-OSI Fair Source license"**: [.github/CONTRIBUTING.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/.github/CONTRIBUTING.md).
- **OSD criteria 1, 3, 6, 7**: [opensource.org/osd](https://opensource.org/osd), OSD text version 1.9 dated 2007-03-22; the criterion-5 mapping is my reading.
- **fair.io definition, DOSP "bright line", "usually after two years", 2015 license disqualified, 13 companies, three licenses**: [fair.io/about](https://fair.io/about/), [fair.io/faq](https://fair.io/faq/), [fair.io/licenses](https://fair.io/licenses/), 2026-09-11.
- **2015 Fair Source License per-user "Use Limitation" and Mitchell's OSD verdict**: [Kyle Mitchell, 30 March 2016](https://writing.kemitchell.com/2016/03/30/First-Read-of-the-Fair-Source-License). The archived 2015 fair.io page shows the v0.9 License Grant covering copyright and patent rights to use, copy, distribute and prepare derivative works subject to the Use Limitation, [web.archive.org, 24 Nov 2015](http://web.archive.org/web/20151124190446/https://fair.io/).
- **FSL-1.1-MIT, Elastic 2.0 and BUSL 1.1 terms in the comparison table**: [FSL template](https://raw.githubusercontent.com/getsentry/fsl.software/main/FSL-1.1-MIT.template.md), [Elastic License 2.0](https://www.elastic.co/licensing/elastic-license), [BUSL 1.1](https://mariadb.com/bsl11/).
- **Four setup profiles; gpt-5.6-luna constant; `max` to `xhigh`; hand-only providers**: [setup.py](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/praxist/cli/setup.py), [_env.py](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/praxist/cli/_env.py), [agent-runtimes.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/agent-runtimes.md), main on 2026-09-11.
- **"does not promise a fixed token or billing reduction", "no model name alone guarantees a high cache-hit rate"**: [cost-optimization.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/cost-optimization.md), [open-source-model-apis.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/open-source-model-apis.md).
- **costs.md "API provider invoices" line; budget-policies.md "dynamic" and "result preservation"**: [costs.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/costs.md), [budget-policies.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/guides/budget-policies.md).
- **The paper never mentions Codex, ChatGPT, OpenAI, subscription, gpt-5 or Luna**: string search of the arXiv HTML; the only "GPT" strings are the MetaGPT and GPTSwarm citations.
- **OpenAI plan prices, Luna allowances, feature matrix; no dollar price per ChatGPT credit**: [learn.chatgpt.com/docs/pricing](https://learn.chatgpt.com/docs/pricing) and [help article 12642688](https://help.openai.com/en/articles/12642688), 2026-09-11.
- **"API keys are still the recommended default for automation."; Luna "lowest cost in the family"**: [learn.chatgpt.com/docs/auth](https://learn.chatgpt.com/docs/auth); [learn.chatgpt.com/docs/models](https://learn.chatgpt.com/docs/models).
- **Pro 20x pause from September 10, 2026; Terms of Use effective January 1, 2026**: [help.openai.com article 9793128](https://help.openai.com/en/articles/9793128-about-chatgpt-pro-plans), [openai.com/policies/terms-of-use](https://openai.com/policies/terms-of-use/). Neither addresses unattended SDK runs; no breach is claimed.
- **Issues #81 to #85: filed 2026-09-01 15:52:16Z to 15:52:21Z by one account, praxist 0.5.0 commit aa198d0, macOS 27.0 arm64, Python 3.14, "agent_runtime:codex_sdk via the run-scoped relay to an OpenAI-compatible provider"; all open on Sep 11, four with zero comments, no maintainer reply**: [issues API](https://api.github.com/repos/sapientinc/PRAXIST/issues?state=all&per_page=100&sort=created&direction=asc), [#81](https://github.com/sapientinc/PRAXIST/issues/81), [#82](https://github.com/sapientinc/PRAXIST/issues/82), [#83](https://github.com/sapientinc/PRAXIST/issues/83), [#84](https://github.com/sapientinc/PRAXIST/issues/84), [#85](https://github.com/sapientinc/PRAXIST/issues/85). Single-operator reports; the reporter is not named here, and which provider sat behind the relay is not stated in the reports I read.
- **PRs #109 and #111 closed 2026-09-03 into validation/budget-and-cost-accounting, 10 commits ahead, unmerged; the "not because the contribution was rejected" closing comment; "commonly takes one to three days"**: [PR #109](https://github.com/sapientinc/PRAXIST/pull/109) and its comment thread, read via the GitHub API on 2026-09-11.
- **CI matrix: ubuntu-latest, Python 3.11 and 3.12**: [ci.yml](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/.github/workflows/ci.yml).
- **Medium test: Adult Income, logistic regression, 2M tokens, degree-two features better on all five development runs, disclaimer quote**: [Medium post, Aug 30, 2026](https://medium.com/@rohanprichard/i-tried-praxist-by-sapient-labs-on-an-ml-dataset-ce6baecfa6ae); self-reported, no hardware, provider or dollar figure; the exact metric values sit in an embedded image and are not reproduced here.
- **WhatJustShipped installed 0.5.0, ran doctor, ran no research loop; the curiosity-about-a-paper line**: [whatjustshipped.com](https://whatjustshipped.com/praxist-gained-5800-stars-in-five-days-and-it-is-not-open-source/), published 2026-09-02.
- **Discord 1,413 members**: [invite API](https://discord.com/api/v9/invites/sapient?with_counts=true), 2026-09-11; channel content not read.
- **ML template: cohort 8, 20 generations, 2.0 h, three "Use This For" bullets, SystemExit evaluator, "Do not invent baseline numbers"**: [task.yaml](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/templates/tasks/machine_learning_template/task.yaml), [README](https://github.com/sapientinc/PRAXIST/blob/main/templates/tasks/machine_learning_template/README.md), [run.py](https://github.com/sapientinc/PRAXIST/blob/main/templates/tasks/machine_learning_template/evaluations/primary/run.py), [baselines README](https://github.com/sapientinc/PRAXIST/blob/main/templates/tasks/machine_learning_template/assets/baselines/README.md). The 40-hour ceiling is 20 x 2.0 h, my arithmetic.
- **Evaluator contract, metric-direction rule, one-unit canary, six prerequisites, baseline-path wording, "one public command" layout, "must not download, install, or provision"**: [task-projects.md](https://github.com/sapientinc/PRAXIST/blob/main/docs/guides/task-projects.md), [first-task.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/first-task.md), [templates README](https://github.com/sapientinc/PRAXIST/blob/main/templates/README.md), [paper](https://arxiv.org/html/2608.25955). The scheduling and pricing fit is my reading of that contract.
- **sam_optimizer is ResNet-18 on CIFAR with a CUDA requirement (task.yaml 26,719 bytes, evaluator 22,722 bytes, benchmark 71,222 bytes)**: [sam_optimizer README](https://github.com/sapientinc/PRAXIST/blob/main/templates/tasks/sam_optimizer/README.md).
- **Rocket example CUDA path, 16 peers, 30 generations, 90-minute fixed window (promote_top_k 4 is in the example's task_GPU_server/task.yaml, read from a checkout), task_PC null baselines "never been measured on a personal computer", 8x H100 80GB measurement host**: [rocket README](https://github.com/sapientinc/PRAXIST/blob/main/examples/rocket_booster_recovery/README.md), [task_GPU_server resource_plan.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/examples/rocket_booster_recovery/task_GPU_server/assets/resource_plan.md). The 45-hour ceiling is 30 x 90 minutes, my arithmetic.
- **Issue #189: stale FROZEN_HASHES pin at evaluator line 58 on main and in the PyPI sdist; no fix PR**: [#189](https://github.com/sapientinc/PRAXIST/issues/189), hashes re-checked from source on 2026-09-11, not by executing a canary.
- **Platform matrix; WSL absent from platform-support.md, installation.md, quickstart.md and the README**: [platform-support.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/operations/platform-support.md). A repository-wide code search for "WSL" would not run for me, so the absence claim is limited to those four files. Nothing was run on Windows or WSL.
- **Install one-liners, "roughly 100-150 MB", uninstall never removes artifacts; the [agents] and [codex] extras pin both the Anthropic and OpenAI SDKs**: [installation.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/installation.md), [pyproject.toml](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/pyproject.toml).
- **Wizard stages, "Nothing is preselected", masked key entry, `codex --yolo` / `claude --dangerously-skip-permissions`, "Other supported ... profiles" sentence**: [quickstart.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/getting-started/quickstart.md).
- **Issue #3 and PR #10 merged 2026-08-31, on main only**: [#3](https://github.com/sapientinc/PRAXIST/issues/3), [PyPI](https://pypi.org/pypi/praxist/json).
- **Privacy Notice version 3: opt-in, four event types, endpoint on theaiscientist.com, 180 days, stated Johor location, no deletion interface, `product-usage status --json` and `product-usage withdraw` commands**: [PRIVACY.md](https://raw.githubusercontent.com/sapientinc/PRAXIST/main/docs/legal/PRIVACY.md); the endpoint resolves to a range registered in Singapore, which neither confirms nor refutes the stated location.
- **Issue #84 key leak on the relay path, PR #196 opened 2026-09-10 and unmerged on Sep 11**: [#84](https://github.com/sapientinc/PRAXIST/issues/84).
- **HRM: arXiv 2506.21734 v1 June 26, 2025, 27M parameters, 40.3%; ARC Prize Aug 15, 2025: 32% ($148.50), 2% ($201), transformer within about 5 points, outer loop +13, 31% vs 41%, no data leakage; TRM 7M parameters 45%/8%; HRM 12,637 stars, Apache-2.0; HN 339 points**: [HRM paper](https://arxiv.org/html/2506.21734v3), [arXiv abs](https://arxiv.org/abs/2506.21734), [ARC Prize](https://arcprize.org/blog/hrm-analysis), [TRM](https://arxiv.org/abs/2510.04871), [HRM repo API](https://api.github.com/repos/sapientinc/HRM), [HN Algolia](https://hn.algolia.com/api/v1/search?query=%22hierarchical%20reasoning%20model%22&tags=story). ARC Prize dates the HRM paper to June 8, 2025; the arXiv v1 date is used here.
- **Co-founder statements (300 to 500 findings per run; single laptop)**: [NetEase Hao, 2026-09-10](https://m.163.com/dy/article/L6FV5P8H05118UGF.html); self-reported, no run data. The same co-founder told [NetEase Tech on 2026-09-11](https://www.163.com/tech/article/L6HSKI5K00098IEO.html) the tool is not meant to replace researchers.
- **Star curve**: Wayback [2026-08-28 02:01 UTC](https://web.archive.org/web/20260828020114/https://github.com/sapientinc/PRAXIST), [2026-09-01 07:48](https://web.archive.org/web/20260901074831/https://github.com/sapientinc/PRAXIST), [2026-09-02 11:37](https://web.archive.org/web/20260902113717/https://github.com/sapientinc/PRAXIST); [RepositoryStats](https://repositorystats.com/sapientinc/praxist) series 6,846 (Sep 3 21:51Z), 6,770, 6,301, 6,385, 6,489, 6,605, 6,249 (Sep 10 01:21Z), 6,559 (Sep 11 08:22Z); GitHub API 6,561 at about 17:00Z. 6,766 minus 6,561 is 205 against the September 11 read I use (206 against the earlier 6,560 read). No Wayback captures exist between Sep 2 and Sep 11; GitHub's per-user stargazer history would not open for me for any repository I tried. No cause is attributed.
- **HN: two submissions on 2026-08-28 with 1 point and 0 comments, both accounts created 2026-08-21, one handle matching an initial-commit co-author trailer**: [HN Algolia](https://hn.algolia.com/api/v1/search?query=%22praxist%22&tags=story&advancedSyntax=true) and the HN user records; handles omitted.
- **Reddit thread: 0 points, 6 comments, top comment at 5 upvotes**: [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/1w0k4yr/praxist_from_sapient_intelligence/), which would not open for me directly; I read it through a copy and Reddit's own feed on 2026-09-11.
- **Absent from GitHub Trending Python Aug 28 to Sep 11 in two archives**: [larsbijl/trending_archive](https://raw.githubusercontent.com/larsbijl/trending_archive/master/2026-09/2026-09-02.md) and bonfy/github-trending.
- **Paid-partnership-labeled launch-day post (6,209 views, 18 likes) and promo-bio quote-posts**: X status IDs 2093155760905838821 and 2093152961837076955, read in a logged-in browser on 2026-09-11; the accounts are deliberately not named or linked here, since the URLs carry the handles.
- **YouTube 74,343 views on a 1.34K-subscriber channel; next video 637**: [Introducing PRAXIST Beta](https://www.youtube.com/watch?v=8BcUyd6s_Ek), counts as displayed on 2026-09-11.
- **No replication or third-party audit found**: search of all 42 issues and PRs, HN Algolia, GitHub-wide issue search and Reddit; stated as "none found", not "none exists". Web search for this piece was limited, so absence claims are bounded by the sources named.
- **Not accessed**: the Business Wire original, the Discord channels, the full jobshop.zip, and any run of Praxist on any machine of mine. Nothing in this post describes a run I performed.

*This post is informational, not legal, financial, or security advice. Mentions of third parties are nominative fair use. No affiliation is implied.*


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