Revenue Operations @ DealHub.io | CPQ, CLM & Digital DealRooms

I've learned from my experience in outbound SDR roles many techniques and mindsets that help me effectively reach out to cold leads at the executive level and create high-quality pipeline. 

My specialty is in Enterprise/Strategic segments and speaking with senior executives, especially via cold call. 

My thesis: today's outbound leads are tomorrow's inbound leads. 
Catch them early and you might influence the requirements, buying criteria, and which alternatives get evaluated. 
Wait and you might miss the train altogether.

Shalhevet Engelson

Richard Makara

CEO @ reconfigured | eradicating inconsistent metrics

GTM Engineer-for-hire. 

Returning to GTM/Growth Engineering after a long hiatus and 3 years as a tech startup founder. 

Love to chat with people about this topic. DM me

--

From a previous life: 

building reconfigured - a quick-capture journal for overloaded minds juggling tabs, tasks, and tools. soon also with MCP capabilities.

sharing progress as I go, usually in the form of neat tricks/productivity gains I've learned along the way.

-- 

Have had the privilege of building the growth engine of a 🦄 company, as well as completely rebuilding CRMs from scratch (Salesforce, HubSpot). 

Some others stats:

- Triangle shaped person - Design, marketing & programming
- B2B SaaS growth + tech stack building.
- Grandfather of Junction, Europe's leading Hackathon 
- Board alumni of Aalto Entrepreneurship Society
- Occasionally tinkering on app ideas with Flask/TS-Node, ReactJS, PostgreSQL & Docker. 
- In a love/hate relationship with TypeScript.

Member

i asked claude to solve the 2025 Clay Cup final round challenge. 😄

... with the help of this little app ive been building.

it let's you operate clay through claude code via MCP, probably other interfaces too. 

Video is edited for entertainment, the real process took around 8 minutes. 

±4 minutes to figure out initial setup, ±3 minutes to self-iterate its own output.

looking for people who use claude (desktop or code) AND clay on daily/weekly basis. 

especially now that my infra doesn't blow up everytime Mikhail boots up his terminal. 😂

Drop a comment or tag a friend if interested - aim to onboard max new 10 users for now.

Building an MCP server is easier than you think. Making it efficient is a little bit harder. Here's my top "crash and learn" bits from the last month of developing an mcp-first app.

1. Building Tools

Main learning I had was: group tools by job, not action.

Started with 15 tools. Claude spent tokens deciding which to use, descriptions bloated the context window, and half had overlapping responsibilities. Reduced tool count to 5

- Each tool should map to a distinct job. My `do` tool handles create, update, delete, run, rename — all mutations under one roof. Claude doesn't pick between `create_column` vs `update_column`. It calls `do` with an action parameter
- Essentially you create simple to understand group tool and nest all your individual bits underneath it.
- Every user action should map to exactly one tool. If something fits two, you get the spiderman meme.

2. MCP Servers can have system prompts 🤡

MCP servers can inject instructions that shape how the model uses your tools across an entire session. It essentially tells a claude how to think about using the tools available.

- My ~800 words of instructions teach Claude to verify data before acting, evaluate results after mutations, and build incrementally instead of chaining blind
- This turned Claude from a tool-caller into something closer to a domain expert

3. Less Tokens, More Fun

A single data fetch on a 50-column table can return ~10,000 tokens. Claude usually needs 2-5 columns. The rest is waste filling up context.

Fix: Throw data into temp memory instead of the context window. Resolve only what you need. Bernardo from Flow AI wrote a super good article on this.

- Return a pointer + "glimpse" (row count, column names, sample values). Claude reads the glimpse, decides what it needs, resolves specific columns on demand. 80-95% token reduction
- The pointer store is just an in-memory Map. Data already flows through the server — I just stopped discarding it immediately. Zero infrastructure needed

4. Don't put LLMs where deterministic logic works

I had Gemini routing every query to classify it into a query type. 4/4 misclassifications observed in testing.

- Replaced it with Claude specifying the query type directly in the tool call. $0/call, 200ms, zero misclassifications
- LLMs add latency, cost, and non-determinism. Use them only where synthesis genuinely adds value

5. The brain is markdown knowledge, not model

My strategy tool uses Gemini Flash ($0.01/call) over a curated knowledge base. 20x cheaper than Sonnet, same quality for instruction-following tasks.

- The knowledge base is the brain — the model just applies it. Upgrading the knowledge improves output. Upgrading the model doesn't
- Very important: Choose a model that is good at following instructions, not at debating.

upside of building clay tables via MCP: you get a lot of things done.
downside of building clay tables via MCP: you can get unwanted things done if you blink too long 😂

lord knows how many versions of "confirm before doing anything" ive written into claude md and still have to hit esc like its a toddler on a run.

taplio

The Battle

Shalhevet Engelson

Richard Makara

Followers

Posts

Engagements

Likes

Performance ScoreRichard Makara's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Shalhevet Engelson

Richard Makara

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Richard Makara's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreRichard Makara's LinkedIn Performance