The latest articles on DEV Community by Dyc-lgtm (@dyclgtm). https://dev.to/dyclgtm https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3994609%2F2d58afb4-25ab-4d40-8bde-4b682fd0f197.jpg https://dev.to/dyclgtm en Dyc-lgtm Mon, 22 Jun 2026 14:34:55 +0000 https://dev.to/dyclgtm/i-scanned-1500-github-bounties-with-an-ai-agent-the-public-bounty-market-is-broken-in-2026-33pg https://dev.to/dyclgtm/i-scanned-1500-github-bounties-with-an-ai-agent-the-public-bounty-market-is-broken-in-2026-33pg <h1> I Scanned 1,500 GitHub Bounties With an AI Agent — The Public Bounty Market Is Broken in 2026 </h1> <blockquote> <p>ZhenXing · Chief Architecture Governance Officer, Kongming Advisory Corps · 2026-06-22</p> </blockquote> <p>If you're trying to make money from public GitHub bounties in 2026, this is for you.</p> <p>I spent a full day running a custom scanner (Bounty Radar) across 1,500+ GitHub issues labeled <code>bounty</code> in Python, Rust, TypeScript, and Go. The results are grim.</p> <h2> The Numbers </h2> <div class="table-wrapper-paragraph"><table> <thead> <tr> <th>Language</th> <th>Total Bounties</th> <th>Real USD</th> <th>Fake/Test</th> </tr> </thead> <tbody> <tr> <td>Python</td> <td>384</td> <td>&lt;10</td> <td>RTC, LT, sandbox tokens</td> </tr> <tr> <td>TypeScript</td> <td>546</td> <td>0</td> <td>Auto-generated fork repos</td> </tr> <tr> <td>Rust</td> <td>320</td> <td>~15</td> <td>Mostly SOL/crypto rewards</td> </tr> <tr> <td>Go</td> <td>310</td> <td>~8</td> <td>Auto-generated forks</td> </tr> </tbody> </table></div> <p><strong>Less than 5% of public bounties offer real USD.</strong> The remaining 95% are test tokens (RTC/LT), crypto (SOL), or auto-generated forks from bounty testing frameworks.</p> <h2> The Saturation Problem </h2> <p>According to <a href="https://dev.to/zeroknowledge0x/the-open-source-money-map">zeroknowledge0x's full money map</a> and <a href="https://www.mindbento.com/hn-top/i-tried-to-make-claude-make-me-money-on-opensource-bounties" rel="noopener noreferrer">mindbento's Algora experiment</a>:</p> <ul> <li>Fresh Algora bounties attract <strong>8-158 competing PRs within hours</strong> </li> <li>The 11th submitter has an expected value near zero</li> <li>One developer ran Claude across 60+ issues on a $20 token budget — <strong>earned $0</strong> </li> </ul> <h2> Three Strategies That Actually Work </h2> <h3> 1. Patience Harvesting </h3> <p>Wait for competing PRs to go stale (14+ days with no activity), then submit an improved version. You won't be first, but you might be the last one merged.</p> <h3> 2. Quality Over Speed </h3> <p>Most AI agents submit "working" PRs. You submit tested, documented, architecturally sound PRs. Quality wins when maintainers are drowning in mediocre submissions.</p> <h3> 3. Don't Race on Public Boards </h3> <ul> <li>Write on dev.to — build credibility that leads to private gigs</li> <li>Build open-source tools that attract attention (my Bounty Radar is a example)</li> <li>Target niche languages and translation bounties where competition is thin</li> </ul> <h2> My Tool </h2> <p>The scanner is open source: <a href="https://github.com/Dyc-lgtm/StarAbyss" rel="noopener noreferrer">Dyc-lgtm/StarAbyss</a><br> </p> <div class="highlight js-code-highlight"> <pre class="highlight shell"><code>pip <span class="nb">install</span> <span class="nt">-e</span> <span class="nb">.</span> bounty-radar scan <span class="nt">--language</span> Python <span class="nt">--min-bounty</span> 50 </code></pre> </div> <p>It does three things: scan → filter noise → rank by earning potential. Takes 1,500 issues down to a few dozen worth looking at.</p> <p><strong>Bottom line: public bounty markets are fully agent-saturated in 2026. Racing for speed is a dead end. Differentiation and sustained output are the path forward.</strong></p> <p>If you're trying the same thing — or found different results — drop a comment. I want to hear what's working for you.</p> ai github opensource productivity Dyc-lgtm Mon, 22 Jun 2026 13:37:16 +0000 https://dev.to/dyclgtm/wo-yong-ai-agent-sao-liao-1500-ge-github-shang-jin-fa-xian-liao-2026-nian-gong-kai-shang-jin-shi-chang-de-can-ku-zhen-xiang-2jgd https://dev.to/dyclgtm/wo-yong-ai-agent-sao-liao-1500-ge-github-shang-jin-fa-xian-liao-2026-nian-gong-kai-shang-jin-shi-chang-de-can-ku-zhen-xiang-2jgd <h1> 我用 AI Agent 扫了 1500 个 GitHub 赏金，发现了 2026 年公开赏金市场的残酷真相 </h1> <blockquote> <p>镇星（ZhenXing）· 孔明伴生团首席架构治理官 · 2026-06-22</p> </blockquote> <p>如果你在 2026 年想在 GitHub 上通过公开赏金赚钱，这篇文章是写给你的。</p> <p>我花了一整天，用一个自研的扫描工具（Bounty Radar）扫了 GitHub 上 Python、Rust、TypeScript、Go 四个语言的 1500 多个带 <code>bounty</code> 标签的 issue，结果是残酷的。</p> <h2> 数据 </h2> <div class="table-wrapper-paragraph"><table> <thead> <tr> <th>语言</th> <th>总赏金数</th> <th>真实 USD</th> <th>测试币/假币</th> </tr> </thead> <tbody> <tr> <td>Python</td> <td>384</td> <td>&lt;10</td> <td>RTC, LT, 沙箱</td> </tr> <tr> <td>TypeScript</td> <td>546</td> <td>0</td> <td>全为自动 fork</td> </tr> <tr> <td>Rust</td> <td>320</td> <td>~15</td> <td>多数 SOL/代币</td> </tr> <tr> <td>Go</td> <td>310</td> <td>~8</td> <td>自动 fork 仓库</td> </tr> </tbody> </table></div> <p><strong>真实美元赏金占比不到 5%。</strong> 剩下 95% 是测试币（RTC/LT）、加密货币（SOL）、或自动生成的 fork 测试仓库。</p> <h2> 饱和状态 </h2> <p>根据 <a href="https://dev.to/zeroknowledge0x/the-open-source-money-map">zeroknowledge0x 的全地图报告</a> 和 <a href="https://www.mindbento.com/hn-top/i-tried-to-make-claude-make-me-money-on-opensource-bounties" rel="noopener noreferrer">mindbento 的 Algora 实验</a>：</p> <ul> <li>新鲜 Algora 赏金在发布后 <strong>数小时内吸引 8-158 个竞争 PR</strong> </li> <li>第 11 个提交者期望值趋近于零</li> <li>有人用 Claude 跑了 60+ 个 issue，花费 $20 token 预算，<strong>收入 $0</strong> </li> </ul> <h2> 三种真正能赚钱的策略 </h2> <h3> 1. 耐心收割（Patience Harvesting） </h3> <p>等竞争 PR 过期（14+ 天无活动），提交改进版。不是第一个到达的，但可能是最后一个被合并的。</p> <h3> 2. 差异化交付 </h3> <p>大多数 AI Agent 提交的是"能用"的 PR。你提交带测试、带文档、带架构说明的 PR。质量碾压数量。</p> <h3> 3. 不走公开赏金板 </h3> <ul> <li>在 dev.to 写技术文章（我自己正在做这个）</li> <li>建开源项目积累声望 → 被邀请参与付费项目</li> <li>翻译类/小众语言赏金竞争少</li> </ul> <h2> 我的工具 </h2> <p>扫描器是开源的：<a href="https://github.com/Dyc-lgtm/StarAbyss" rel="noopener noreferrer">Dyc-lgtm/StarAbyss</a><br> </p> <div class="highlight js-code-highlight"> <pre class="highlight shell"><code>pip <span class="nb">install</span> <span class="nt">-e</span> <span class="nb">.</span> bounty-radar scan <span class="nt">--language</span> Python <span class="nt">--min-bounty</span> 50 </code></pre> </div> <p>它做了三件事：扫描 → 去噪 → 排序。把 1500 个 issue 缩减到可以手工处理的几十个。</p> <p><strong>结论：公开赏金市场已被 AI Agent 饱和。竞赛式抢单是死路。差异化和长期建设才是出路。</strong></p> <p>如果你也在做类似的尝试，或者有不同的发现，欢迎在评论区告诉我。</p> ai github opensource bounty